51ºÚÁϲ»´òìÈ

4.2.5 Analyze Google Analytics Data using Customer Journey Analytics

Objectives

4.2.5.1 Create a Connection

Go to to access Customer Journey Analytics.

On the Customer Journey Analytics homepage, go to Connections.

Here you can see all the different connections made between CJA and Platform. These connections have the same goal as report suites in 51ºÚÁϲ»´òìÈ Analytics. However, the collection of the data is totally different. All data is coming from 51ºÚÁϲ»´òìÈ Experience Platform datasets.

Click Create new connection.

You’ll then see the Create Connection UI.

For the name, use this : --aepUserLdap-- - GA + Loyalty Data Connection.

You need to select the correct sandbox to use. In the sandbox menu, select your sandbox, which should be --aepSandboxName--. In this example, the sandbox to use is Tech Insiders.

Set the Average number of daily events to less than 1 million.

In the dataste menu, you can now start adding datasets. Click Add datasets.

The datasets to add are:

Search for both datasets, check their checkbox and then click Next.

You’ll then see this:

For the dataset Demo System - Event Dataset for BigQuery (Global v1.1), change the Person ID to loyaltyId and set the Data source type to Web Data. Enable both options for Import all new data and Backfill all existing data.

For the dataset Demo System - Event Dataset for BigQuery (Global v1.1), verify that the Person ID is set to to crmId and set the Data source type to Web Data. Enable both options for Import all new data and Backfill all existing data. Click Add datasets.

You’ll then be here. Click Save.

After having created your Connection it may take a few hours before your data is available in CJA.

You’ll then see your connection in the list of available connections.

4.2.5.2 Create a data view

With your connection done, you can now progress to influencing visualization. A difference between 51ºÚÁϲ»´òìÈ Analytics and CJA, is that CJA needs a data view in order to clean and prepare the data before visualization.

A data view is similar to the concept of virtual report suites in 51ºÚÁϲ»´òìÈ Analytics, where you define context-aware visit definitions, filtering and also, how the components are called.

You’ll need a minimum of one data view per connection. However, for some use-cases, it’s great to have multiple data views for the same connection, with the goal of giving different insights to different teams.

If you want your company to become data-driven, you should adapt how data is viewed in each team. Some examples:

On the Connections screen, check the checkbox in front of the connection you just created. Click Create data view.

You’ll be redirected to the Create Data View workflow.

You can now configure the basic definitions for your data view. Things like Time zone, Session Timeout or the data view filtering (the segmentation part similar to Virtual Report Suites in 51ºÚÁϲ»´òìÈ Analytics).

The Connection you created in the previous exercise is already selected. Your connection is named --aepUserLdap-- - GA + Loyalty Data Connection.

Next, give your data view a name following this naming convention: --aepUserLdap-- - GA + Loyalty Data View.

Enter the same value for the description: --aepUserLdap-- - GA + Loyalty Data View.

Before doing any analysis or visualization we need to create a data view with all the fields, dimensions and metrics and their attribution settings.

Click Save and continue.

Click Save.

You can now add components to your data view. As you can see, some metrics and dimensions are added automatically.

Add the below components to the data view. Also make sure to update the field names to friendly names. To do that, select the metric or dimension and update the Component name field in the right menu.

You’ll then have something like this:

Next, you need to make some changes to the Person and Session context for some of these components by changing the Attribution or Persistence Settings.

Please change the Attribution Settings for the below components:

To do that, select the component, click Use custom attribution model and set the Model to Most Recent, and the Expiration to Person Reporting Window. Repeat this for all of the above mentioned components.

After making the changes in attribution settings for all of the above mentioned components, you should then have this view. Click Save and continue.

On the Settings screen, no changes are required. Clickl Save and finish.

You are now ready to to analyze Google Analytics data within 51ºÚÁϲ»´òìÈ Analytics Analysis Workspace. Let’s move to the next exercise.

4.2.5.3 Create your Project

In Customer Journey Analytics, go to Workspace. Click Create Project

Select Blank Workspace Project and click Create.

You now have a blank project:

First, save your project and give it a name. You can use the following command to save:

You’ll see this popup. Please use this naming convention:

Next, click Save.

Next, make sure to select the correct data view in the upper right corner of your screen. This is the data view you created in the previous exercise, with the naming convention --aepUserLdap-- - GA + Loyalty Data View.

4.2.5.3.1 Freeform Tables

Freeform tables work, more or less, as pivot tables within Excel. You pick something from the left bar and you drag and drop it into the Freeform and you’ll get a table report.

Freeform tables are almost limitless. You can do (almost) anything and this brings so much value when compared to Google Analytics (since this tool has some analysis limitations). This is one of the reasons to load Google Analytics data into another analysis tool.

Let see two examples where you need to use SQL, BigQuery and some time to answer simple questions that are not possible to do within the Google Analytics UI or Google Data Studio:

Let’s answer this questions and some more with Analysis Workspace in CJA.

First, select the right date range (Today) on the right side of the panel. CLick Apply.

Let’s drag and drop some dimensions and metrics to analysis the Marketing channels. First use the dimension Marketing Channel and drag and drop it to the canvas of the Freeform table. (Click on Show All in case you don’t see the metric immediately in the Metrics menu)

You’ll then see this:

Next, you need to add the Metrics to the Freeform Table. You should add these Metrics: People, Sessions, Product Views, Checkouts, Purchases, Conversion Rate (Calculated Metric).

Before you can do that, you need to create the Calculated Metric Conversion Rate. To do that, click the + icon next to Metrics:

As a name for the Calculated Metric, use Conversion Rate and use conversionRate for External ID. Then drag the Metrics purchase and Sessions onto the canvas. Set Format to Percent and Decimal Places to 2. Finally, click Save.

Click Save.

Next, in order to use all these Metrics in the Freeform Table, drag and drop them one by one onto the Freeform Table. See the example below.

You’ll end up with a table like this one:

As mentioned above, Freeform tables give you the freedom you need to perform deep dive analysis. For instance, you can pick any other Dimension to break down a specific Metric inside the table.

As an example, go to dimensions and search and select the Browser variable.

You’ll then see an overview of available values for this Dimension.

Pick the Dimension Safari and drag and drop it on top of a Metric, for example Checkouts. You’ll then see this:

Doing this, you just answered a potential question you had: How many people arrive to the checkout page using Safari, split by Marketing Channel?

Let’s now answer the Attribution question.

Find the Purchase metric in the table.

Hover over the metric and a Settings icon will appear. Click it.

A contextual menu will appear. Check the checkbox for non-default attribution model.

In the popup you’ll see, you can easily change the attribution models and lookback window (which is quite complex to achieve with SQL).

Select First Touch as your attribution model.

Choose Person for the Lookback Window.

Now click Apply.

You can now see that the attribution model for that particular metric is now First Touch.

You can do as much breakdown as you want, without limits of types of variable, segments, dimension or date ranges.

Something even more special is the ability to join any dataset from 51ºÚÁϲ»´òìÈ Experience Platform to enrich the digital behavioural data from Google Analytics. For example, offline, call center, loyalty or CRM data.

To showcase that functionality, let’s configure your first breakdown thats combines offline data with online data. Pick the dimension Loyalty Level and drag and drop it onto any Marketing Channel, for instance, Organic Search:

Next, let’s analyze which Device Type is used by customers that came to the site using Organic Search with a Loyalty Level that is Bronze. Take the Dimension Device Type and drag and drop it onto Bronze. You’ll then see this:

You can see that for your first breakdown, Loyalty Level is used. This dimension comes from a different dataset and different schema than the one that you used for the BigQuery connector. The Person ID loyaltyID (Demo System - Event Schema for BigQuery (Global v1.1)) and loyaltyID (Demo System - Profile Schema for Loyalty (Global v1.1)) match with each other. Therefor, you can combine Experience Events from Google Analytics with Profile Data from the Loyalty Schema.

We can keep splitting the rows with segments or specific date ranges (maybe to reflect particular TV campaigns) to ask questions to Customer Journey Analytics and get the answers on the go.

Achieving the same end result with SQL and then a third-party visualization tool is quite a challenge. Especially when you’re asking questions and trying to get the answers on the fly. Customer Journey Analytics doesn’t have this challenge and allows Data Analysts to query the data flexibly and in real-time.

4.2.5.3.2 Funnel or fallout analysis

Funnels are a great mechanism to understand the main steps in a customer journey. These steps can also come from offline interactions (for example, from the call center) and then you can combine them with digital touch-points in the same funnel.

Customer Journey Analytics allows you to do that and much more. If you remember Module 13, we where able to right click and do things like:

Let’s see another thing you can do: How is my Customer Journey Funnel this month against the previous month? What about mobile vs desktop?

Below you’ll create two panels:

You’ll see that we are comparing a funnel over different periods of time (January and February) split by Device Type.

This type of analysis is not possible within the Google Analytics UI, or is very limited. So CJA again adds lot of value to the data captured by Google Analytics.

To create your first fallout visualization. Please close the current panel to start from with a new one.

Look at the right side of the panel and click the arrow to close it.

Next, click + to create a new panel.

Now select the Fallout Visualization.

As an analyst, imagine that you want to understand what’s happening with your main ecommerce funnel: Home > Internal Search > Product Detail > Checkout > Purchase.

Let’s start by adding some new steps to the funnel. To do that, open the Page Name dimension.

You’ll then see all available pages that have been visited.

Drag and drop Home to the first step.

As second step, use the Store search results

Now you need to add some ecommerce actions. In the Dimensions, search for the Dimension Event Type dimension. Click to open the dimension.

Select Product_Detail_Views and drag and drop it into the next step.

Select Product_Checkouts and drag and drop it into the next step.

Resize your Fallout visualization.

Your fallout visualization is now ready.

To start analyzing and documenting the insights, it’s always a good idea to a Text visualization. To add a Text visualization, click on the Graph icon in the left menu to see all available visualizations. Then drag and drop the Text visualization onto the canvas. Resize and move it so that it looks like the below image.

And again, resize it to fit the dashboard:

Fallouts visualizations also allow breakdowns. Use the Device Type dimension by opening it and drag and drop some of the values on by one onto the visualization:

You’ll end up with a more advanced visualization:

Customer Journey Analytics allows you to do that and much more. By right-clicking anywhere in the fallout, you can…

As an example, do a right-click in any step of the fallout to see some of these analysis options.

4.2.5.3.3 Flow Analysis & Visualization

If you want to do advanced flow analysis using Google Analytics, you need to use SQL to extract the data and then use a third-party solution for the visualization part. Customer Journey Analytics will help with that.

In this step, you’ll configure a flow analysis to answer this question: What are the main contributing channels before a specific Landing Page. With two drag and drops and one click, as an analyst, you can discover the flow of the user towards the Landing Page with the two last touches of marketing channels.

Other questions that Customer Journey Analytics can help you answer:

Let’s start with a blank panel again to answers these questions. Close the current panel and click +.

Now select the Flow visualization.

Let’s now setup a multi-path Marketing Channel Flow Analysis. Drag and drop the Marketing Channel dimension onto the Entry Dimensions area.

You can now see the first entry paths:

Click the first path to drill down on it.

You can now see the next path (Marketing Channel).

Let’s do a third drill-down. Click on the first option within the new path, Referral.

Now you should see the visualization like this:

Let’s complicate things. Imagine you want to analyze what the landing page was after two marketing paths? To do this you can use a secondary dimension to change the last path. Find the Page Name dimension and drag and drop it like this:

You’ll now see this:

Let’s do another flow analysis. This time you’ll analyze what happened after a specific exit point. Other Analytics-solutions require the use of SQL/ETL and again, a third-party visualization tool to achieve the same thing.

Bring a new Flow Visualization to the panel.

You’ll then have this:

Find the Dimension Event Type and drag and drop it to the Exit dimension area.

Now you can see which Event Type-paths drove customers to the exit.

Let’s investigate what happened before the exit from the checkout-action. Click on the Product_Checkouts path:

A new action path will appear with some data that is not insightful.

Let’s analyze further! Search the Dimension Page Name and drag and drop it to the new generated path.

You have now an advanced flow analysis done in minutes. You can click the different paths to see how they connect from the exit to the previous steps.

You now have a powerful kit to analyze funnels and explore paths of customer behavior across digital but also, offline touch points.

Don’t forget to save your changes!

4.2.5.4 Share the project

FYI - You can share this project with colleagues to collaborate or to analyze business questions together.

Next Step: Summary & benefits

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