Google Ads was introduced in a previous blog post titled Introduction to Google Ads by Alexander Dalessi.
Global Site Tagging
Global site tagging is used to obtain data from all Google products and services. This involves copying and pasting code into the head section of a website’s HTML page. Additional tags are required to monitor traffic from other advertising or media platforms. Additional tags are also required to enable monitoring with other analytics tools. Google Tag Manager can be used to obtain these additional tags for non-Google platforms. In this blog post, the focus will be on data from Google Ads and Google Analytics.
Ad Groups
An ad group contains one or more ads that target a shared set of keywords. Ad groups are extremely helpful to organize ads based on keywords so you can select the right ads for a campaign based on the relevant keywords. Generally, having larger and fewer ad groups split by business objectives consolidates traffic and increases the number of conversions per ad.
Analyzing data from Google Ads and Google Analytics
Once data is obtained, it can be exported as a .csv file. A .csv(comma-separated values) file is a file where commas separate each of the data values contained in the file. A great reason to export data from Google Ads is to keep an archive of data from past campaigns for analysis and comparison. Google Analytics provides many different methods to visualize conversion data, also called explorations in Google Analytics.
A free-form exploration provides charts and tables to visualize all the data. Generally, the data a business or marketer is interested in is conversions. A conversion is when a client-side user does a desired action. A tunnel exploration visualizes the steps towards conversion. A segment overlap exploration visualizes how data segments relate to each other. A cohort exploration organizes the data into groupings based on common attributes. Lastly, a user lifetime exploration visualizes the customer value and behavior over time.
There are many models in which a conversion is tracked in Google Analytics. An ads-preferred model attributes 100% of a conversion to the last Google Ad channel the customer clicked through before a conversion occurred. A cross-channel last-click attribution model gives 100% conversion credit to the last touchpoint(point along the path of a conversion) on a conversion path. A cross-channel model attributes a percentage of conversions to all advertising channels with touchpoints. A cross-channel first-click attribution attributes 100% of the conversion to the first channel a customer clicked through or engaged with before converting. A cross-channel linear attribution model distributes attribution for a conversion equally across all channels a customer clicked through or engaged with.
Custom dimensions through UTM tags can be set up in Google Analytics. UTM(Urchin tracking module) is named after the Urchin Software Company that first developed the model. A UTM tag is added to a URL to help monitor the content. Three common UTM tags are utm_source, utm_campaign, and utm_medium.
Utm_source tags monitor the source of the traffic to the desired landing page. A utm_campaign tag tracks the name of the campaign. Lastly, utm_medium tracks advertising mediums such as email.
In addition to data about conversions, businesses and marketing teams are concerned with ROI(return on investment) and ROAS(return on ad spend). ROI(return on investment) is the ratio of net income(money made) to investment(money spent). The marketing ROI is calculated as the ratio of the sales growth minus the marketing cost and the marketing cost. Return on ad spend or ROAS is the number calculated as the revenue generated divided by the amount spent on advertising. If ROAS targets are not being met, Google suggests four things: lengthening the campaign duration, setting ROAS targets by product groups, reviewing how the initial ROAS target was set, or adjusting the bidding strategy.
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