Performance marketing

Upload campaign cost data into Google Analytics: a new free tool available

Import cost data into Google Analytics

Importing click and cost data for non-Google search engines and campaigns into  allows to compare performance data for Google and non-Google campaigns.

OWOX, a Partner, has created a free add-on that helps you quickly and easily import data from Google Sheets into Google Analytics. It is not the first tool for this scope, but it’s worth mentioning because it is free and easy-to-use.

Here are few simple steps to take to see your non-Google campaign cost data (for example Facebook Ads, Twitter, Linkedin or Bing Ads) into GA Views.

Here is a step-by-step guide:

  1. Create a Cost data schema on GA
  2. Download the schema (Excel template) as guidance. A typical format is:
    ga:datega:mediumga:sourcega:impressionsga:adClicksga:adCost
     ga:campaign ga:adGroup ga:adContent
  3. Export cost data from Facebook Ads
  4. Install the Google Chrome Add-On, it will be available on Google Sheet (you need to access with a Google account that has been granted GA editing rights)
  5. Copy and past FB cost data into Google Sheet making sure that it matches columns of the GA schema (point 2)
  6. Start the Add-On from the menu and upload

Once done, cost data will be visible under GA / Acquisition / Cost Data enabling interesting business metrics.

Two final notes:

If something goes wrong data can be re-uploaded, they will be overwritten.

The cost data has to be daily, and the format of its first column (date) needs to be yyyymmdd.

Last but not least, a perfect correspondence between Facebook Ads URL parameters (medium, source, campaign and content) and the cost data spreadsheet is needed to match data on Google Analytics. Differently, you will find a mismatch between uploaded metrics and Sessions. Ideally, you can add two columns for medium and source while for the others, it’s advised to use the same name both in the URL parameters and in the FB Ads level (for example Adgroup matching Ad set).

 

 

Discrepancy between conversions on Google Analytics and Facebook advertising reports: VTC vs CTC

Digital Marketing Conversions

A digital customer journey: which channel will get (most of) the credit for a conversion? (Image credit: DigitSix.com)

Have you ever encountered a discrepancy between performance reports by channel and what’s reported by advertising platforms’ reports, for example, ? The former is usually less generous than the latter, and you don’t know who’s telling the truth, right?

Let’s imagine a situation when you have been running promoted posts through Facebook and the outcome has been ten conversions according to the Report (measured through the Facebook Conversion Pixel), four Assisted Conversions according to Google Analytics (Multi-Channel funnel report) and no Direct Conversion (still according to Google Analytics). What measure can be considered more reliable?

It depends on what you are trying to sell. If you want to know what is the best target audience, then go for the Facebook report. If you want to know what is the best channel to generate leads go for the Assisted Conversions report. Differently, if you are offering something that should not require much thinking before a conversion, then focus on the Direct Conversion report – where usually Search Marketing performs much better than , in particular if targeting branded keywords.. a quick win!

The difference is explained by the methodology adopted to identify a conversion: Google Analytics takes into account only CTC (Click-Through-Conversions) while other platforms in their reports also show (or only) VTC (View-Through-Conversions).

offers both options, but CTC (more meaningful for search marketing than for display) are deducted from VTC.

Some advertising platforms (like Adroll) make a clear distinction between the two measurement methods, while others (like Facebook) state it in a more subtle way:

Facebook (then) matches that conversion event against the set of people an ad was served to/or that clicked on an ad so that we can provide you with information that helps you understand the return on investment for your ad spend.“

(source: https://www.facebook.com/help/435189689870514)

However, even on Facebook, you can still compare both methods also on Facebook report through the Attribution Window settings (image above).

Facebook Advertising report: Attribution Window settings

Facebook Advertising Report: Attribution Window settings

 

Another aspect to consider is the time frame. Facebook offer three options: 1, 7 and 28 days, while in Google Analytics Multi-Channel Funnel report you have 90 choices, from 1 to 90 days before conversion. Every platform has its options therefore if you don’t synchronise the method, you will get different results.

VTC methodology considers a digital channel like an offline channel (e.g. TV) since it takes into account all conversions completed after someone has seen an ad but has not clicked on it. Some way, it makes sense because a person might discover a brand or a product through a promoted post and still do not take any immediate action (e.g. click, comment, share, etc.) but search for it later through other channels.

Obviously, VTC is more generous towards the platform than CTC, which is a situation that requires the converted user to click on an ad, within a particular period of time. On Google Analytics, Multi-Channel Funnels take into account only CTC.

To make the story short, in a logic sequence, the highest performance regarding Conversion Rate, , , etc. is measured considering VTC, and then CTC (Multi-Channel) and eventually CTC (Direct or last-click).

Digital Conversions: VTC vs CTC

VTC will always include CTC

Digital agencies usually tend to show the best performance in their reports, but despite it might sound obvious to some people, it is always good to ask for a clear “legend” where it’s well explained what is intended by “conversion”.

The methodology applied should always be explicit and come before any attribution model. The best approach, therefore, is to produce different columns to outline the outcome of each methodology applied to determine conversions.