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explore_funnels

Call or Deploy explore_funnels ?

✅ You can call this explore_funnels bigfunction directly from your Google Cloud Project (no install required).

  • This explore_funnels function is deployed in bigfunctions GCP project in 39 datasets for all of the 39 BigQuery regions. You need to use the dataset in the same region as your datasets (otherwise you may have a function not found error).
  • Function is public, so it can be called by anyone. Just copy / paste examples below in your BigQuery console. It just works!
  • You may prefer to deploy the BigFunction in your own project if you want to build and manage your own catalog of functions. This is particularly useful if you want to create private functions (for example calling your internal APIs). Discover the framework

Public BigFunctions Datasets:

Region Dataset
eu bigfunctions.eu
us bigfunctions.us
europe-west1 bigfunctions.europe_west1
asia-east1 bigfunctions.asia_east1
... ...

Description

Signature

explore_funnels(events_table, event_types)

Description

Show funnels (WORK IN PROGRESS)

See the result as a data visualization in BigQuery Console!

The result of this function can be vizualized as an html report directly in BigQuery Console!

  1. Install this bookmarklet: bigfunctions (it has to be done only once)
  2. Open BigQuery console
  3. Click on the installed bookmarklet.
    • From now on, the bookmarklet code will observe the BigQuery console page.
    • If a BigQuery result appears with a unique cell containing html content, it will be rendered.
  4. You will have to click on the bookmarklet again:
    • If you refresh the Bigquery console page,
    • If you open the BigQuery console in a new tab of your browser.
  5. Run the query of the example and open the result of the latest subquery. The result will be shown as a nice html content.


bookmarklet usage

Examples

call bigfunctions.eu.explore_funnels('compte-nickel-dataprod.TEMP.EVENT_TYPE_SNOWPLOW2', ["UserProfile", "PhoneNumberEditionWorkflowIntroduction", "PhoneNumberEditionForm", "PasswordChallenge", "CodeEmailChallenge", "CodeSMSChallenge"]);
select html from bigfunction_result;

call bigfunctions.us.explore_funnels('compte-nickel-dataprod.TEMP.EVENT_TYPE_SNOWPLOW2', ["UserProfile", "PhoneNumberEditionWorkflowIntroduction", "PhoneNumberEditionForm", "PasswordChallenge", "CodeEmailChallenge", "CodeSMSChallenge"]);
select html from bigfunction_result;

call bigfunctions.europe_west1.explore_funnels('compte-nickel-dataprod.TEMP.EVENT_TYPE_SNOWPLOW2', ["UserProfile", "PhoneNumberEditionWorkflowIntroduction", "PhoneNumberEditionForm", "PasswordChallenge", "CodeEmailChallenge", "CodeSMSChallenge"]);
select html from bigfunction_result;
Need help using explore_funnels?

The community can help! Engage the conversation on Slack

For professional suppport, don't hesitate to chat with us.

Found a bug using explore_funnels?

If the function does not work as expected, please

  • report a bug so that it can be improved.
  • or open the discussion with the community on Slack.

For professional suppport, don't hesitate to chat with us.

Use cases

The explore_funnels function is designed to visualize user journeys through a series of events, commonly known as a funnel analysis. Here's a breakdown of a potential use case:

Scenario: An e-commerce website wants to understand user drop-off points during the checkout process.

Events Table: The events_table (e.g., 'compte-nickel-dataprod.TEMP.EVENT_TYPE_SNOWPLOW2' in the example) would contain user activity data. Each row represents a single event with information like:

  • user_id: Unique identifier for each user.
  • event_type: The type of event (e.g., 'Add to Cart', 'Begin Checkout', 'Payment Information', 'Order Confirmation').
  • timestamp: The time the event occurred.

Event Types: The event_types parameter (e.g., ["UserProfile", "PhoneNumberEditionWorkflowIntroduction", "PhoneNumberEditionForm", "PasswordChallenge", "CodeEmailChallenge", "CodeSMSChallenge"]) defines the steps in the funnel the analyst wants to examine. In our e-commerce example, this could be:

  • "Add to Cart"
  • "Begin Checkout"
  • "Payment Information"
  • "Order Confirmation"

How the Function Works:

  1. The function takes the events_table and event_types as input.
  2. It queries the events_table to find occurrences of each event type specified in event_types, ordered by the timestamp.
  3. It calculates the number of users who reached each step in the funnel.
  4. It then generates an HTML visualization of the funnel, showing the number of users at each stage and the drop-off rates between stages. This visualization can be displayed directly within the BigQuery console using the provided bookmarklet.

Insights Gained:

By visualizing the funnel, the e-commerce website can identify where users are abandoning the checkout process. For instance, if there's a large drop-off between "Begin Checkout" and "Payment Information," it might indicate issues with the payment form or a lack of trust in the payment gateway. This insight can then inform improvements to the checkout experience, such as simplifying the payment form, offering more payment options, or highlighting security measures.

Other Use Cases:

Besides e-commerce checkout flows, this function can be applied to various scenarios:

  • SaaS Onboarding: Tracking user progress through a software setup process.
  • Mobile App Usage: Analyzing how users navigate through different screens in an app.
  • Lead Generation: Monitoring the steps leads take from initial contact to conversion.
  • Content Consumption: Understanding how users engage with different pieces of content on a website.

In essence, explore_funnels helps analyze any process that can be broken down into a series of sequential steps, allowing you to identify bottlenecks and optimize user flow.

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