Introduction

In 2018, Google brought its premium advertising tools under a single umbrella: Google Marketing Platform. The move marked a shift towards an integrated approach to advertising, analytics, and optimization. The power of the Google Marketing Platform lies in the strength of its integrations.

In this blog post, I’ll explore the benefits which can be reaped by integrating Google Analytics 360 with Google Marketing Platform advertising products like Search Ads 360, Display & Video 360, and Campaign Manager. I’ll cover several reporting questions and provide integration-powered solutions that you can start using immediately.

How Do I Integrate GMP Products?

Lucky for you, the integration process for Google Analytics 360 and GMP advertising products is quick and simple once you have the proper permissions in place. As this blog post is focused on the analysis opportunities gained from product linking, I will not focus on how to implement the integrations. If you have not already implemented an integration, I encourage you to read the Google Support documentation for your products:

Analytics, Advertising, and Auto-Tagging

Before we dive into the analysis opportunities from product linking, I’d like to take a brief moment to discuss auto-tagging.

When you link a GMP product with Google Analytics, auto-tagging is immediately applied. Auto-tagging ensures that your advertising data comes into Google Analytics in a consistent, usable format. As many organizations employ multiple agencies to run different branches of their paid media, it’s common for data naming conventions to be implemented differently. Auto-tagging forces consistency and makes your data infinitely more usable.

If you are still using manual tagging, be aware that auto-tagging is automatically enabled when you link products.

How do ads contribute to conversions?

Think back to the last time you made a purchase online. Did you think about a brand without any online or offline influence, visit their site once from your desktop, and immediately make the purchase? If you’re like most consumers, the answer is no. Today’s digital ecosystem is complex and the standard customer journey has complexified in turn.

If you’re using a last-click attribution model, you may be significantly undervaluing the role of paid media in the path to conversion.

Enter Data-Driven Attribution in GA360, commonly known as DDA. “Unlike standard position – or rules-based attribution models, Data-Driven Attribution uses actual data from your Analytics account to generate a custom model for assigning conversion credit to marketing touchpoints throughout the entire customer journey.”

Using the Model Comparison Tool report, see the conversion contribution of a channel with different attribution models applied. When comparing the standard Last Interaction model with the Data-Driven model, it’s clear that Display campaigns are significantly undervalued under the standard model. For the top Display campaign for the Google Merchandise Store account, the difference in conversions was over 200%!

Source: Demoverse Account, Report Link

While this example uses the CM Campaign dimension, you can apply others like CM Creative, SA360 Keyword, or DV360 Site. For a full list of GMP dimensions, refer to the Dimensions and Metric Explorer Tool from Google.

How does ad exposure contribute to on-site engagement?

As we saw above, paid media can be undervalued using the traditional last-click model. That model prioritizes ad clicks and ignores the role of ad exposure on site engagement and conversion probability.

If you are using Campaign Manager to serve or track ads, the GA360 integrations with Display & Video 360 and Campaign Manager can provide valuable insight into the ad exposure effect. Ads not served or tracked through Campaign Manager cannot take advantage of this.

Source: Demoverse Account, Report Link

The suite of Campaign Manager and Display & Video 360 reports use the CM Model as opposed to the GA Model. If a user views an ad served/tracked by Campaign Manager and later visits the site via another source, that activity is reflected as a “View Through”. If a user clicks through an ad, that is a “Click Through.” If a user later visits the site via another source, the conversion will be attributed to the View-Through of Click-Through it originated from. Most other reports in Google Analytics use the GA Model which does not include view-through and uses the Last Non-Direct attribution model.

To analyze the impact that ad exposure has on-site behavior, use the GMP Reports or create Google Analytics Segment like the one below. This segment includes users who saw a Campaign Manager ad but did not click on it. You could also create a segment for “CM Attribution Type (CM Model) exactly matches Click-through” and layer both onto reports in Google Analytics outside the Google Marketing Platform suite.

Use the GMP Reports to answer questions like:

  • How do view-through conversions contribute to Pages per Session, Avg. Session Duration, and Bounce Rate? (Site Usage tab)
  • Did a particular placement drive a significant amount of revenue from view-through conversions? (Ecommerce tab)

Use the segments to answer questions like:

  • Did view-through traffic from a particular campaign have a positive impact on conversion drop-off when compared to all site traffic?
  • Did view-through traffic from a specific creative lead to more micro-conversions (like newsletter signups) than all site traffic did?

Who are my high-value customers? How can I better reach customers like them?

Linking GMP products doesn’t just allow for better analysis; it enables insightful data-driven optimization. Using Google Analytics data, you can identify the characteristics of high-value customers, create Audiences, and share them with GMP ad platforms.

Importing audiences into advertising platforms allows you to do two things:

  1. Similar Audiences: The advertising platform will create a “Similar Audience” from the one you imported, which allows you to target new users with shared characteristics to your high-value users.
  2. Take Advantage of On-Site Behavior: While using Floodlights can provide you with targeting based on pages viewed and conversions taken, the amount of insight pales in comparison to your full set of on-site data. Although you cannot create Audiences with demographic data, like age and gender, you still have a large amount of data to choose from.

What defines a “high-value customer” is unique to every business but Google Analytics provides some common dimensions to start with, like Conversion Probability. Conversion Probability uses machine learning to determine a user’s likelihood to convert within the next 30 days and provides a score on a scale from 0 to 100. Scores closer to 100 indicate a stronger propensity to convert.

Source: Demoverse Account, Report Link

What characteristics do users with higher scores have in common compared to those with lower scores? To find out, create segments for users above and below a threshold and analyze GA reports you find interesting.

Let’s say you discover that users with Conversion Probability > 25 typically visit your Product Pages at least 5 times and spend at least 5 minutes per site visit. To optimize your targeting, you need to market more to users similar to those you found in GA.

To do so, you’ll create a segment with these characteristics, import it as a GA Audience, and share with your linked advertising accounts.

If you are using Google Marketing Platform advertising products and are a Google Analytics 360 customer, you should be taking advantage of the available integrations. Once you get started, the amount of analysis and optimization opportunities are nearly infinite. Contact E-Nor today to explore how we can help.

About the Author

Caitlin McCluskey
Digital Analytics Consultant
Shortly after graduating from the University of Colorado at Boulder with Bachelors degrees in English and Psychology, Caitlin discovered her passion for Google Analytics. After working in marketing and search engine optimization, Caitlin made her way into analytics strategy. A lifelong self-starter, her passion for data analysis and data visualization is infectious.