Define how much your impressions are worth with Custom Bidding. Implementing the tool, which has been a game-changer to the programmatic community, allows advertisers to better align their campaign objectives and business goals, thanks to custom algorithms and automated processes. 

What is Custom Bidding?

New functionalities emerge every day that allow us to automate processes, save time and improve the quality of information through machine learning. Custom Bidding is a mixture of these characteristics. Unlike standard or automatic bids, with Custom Bidding, we can customise the algorithm based on real business objectives. Through a script created in DV360, we will provide more insights into the algorithm to apply machine learning optimally and uniquely for each client and campaign.  

What is Custom Bidding, and how does it contribute to our campaigns?

Custom Bidding combines our business insights with Google’s algorithm. This union between campaign insights and objectives, alongside Google’s machine learning, will allow us to align our goals with the advertiser’s business objectives.  

How does it work?

Very simple, you must first ask yourself two questions:

• What is your goal? If your objective is a standard CPM, CB is not necessary. On the contrary, if you want to optimise towards other goals and KPIs, you can consider CB as a solution,
• Do you want to manage the campaign manually? In case your priority is not to manually configure bids, and you prefer that Google’s algorithm maximise the effectiveness of our campaigns, we recommend applying CB.

Secondly, we identify the signals and set our objectives (KPIs). Advertisers must understand what they are measuring and how they are going to optimise based on it. This takes us to step three, designing the script, which does not require a coding expert.

 These are the three main aggregate models; each of them applies to the different use cases that we will discuss below:

• Sum-aggregate: In all cases, add all the weights.
• Max_Aggregate: In all cases, it only considers the maximum weight.
• First_match_aggregate: For all conversions, find the first interaction that occurs within the script sequence.

Once the script has been created in DV360, we must assign a bidding algorithm to our test insertion order (replacing the non-personalised options offered by the platform), the algorithm will optimise based on the rules established in our script. 

Suppose you have already completed the previous steps. In that case, it is time to select your script as the bid algorithm within our insertion order and start checking the results between the test insertion order and the new insertion order with the custom algorithm.  

Use cases

To help us better understand each possible scenario, we have six previously defined use cases:

• Weighted Conversions: All goals contribute to the achievement of the final objective. We can give weights for each step of the conversion funnel, prioritising those actions that have the most value for the advertiser.
• Activity Value: Unlike weighing only based on specific events such as floodlights, this model allows us to optimise based on custom u-variables. We can give different weights to our custom variables collected in the Campaign manager to increase the profit.
• Transaction ROAS: We can use the value of each product or service to adjust sales value. The advantage is differentiating the sales values ​​by product, not all sales are worth the same.
• Google Analytics: With GA360, we can weigh based on different events within our site, as long as we have a premium account and have all those goals configured. For example, users who have placed an order are more valued or those who have spent more time browsing our website.
• Brand Signals: We use branding signals such as viewability, full video, and others to make the most of this knowledge in our campaigns.
• Unified Conversions: With the help of Campaign Manager and Google Analytics, we are able to collect all that information that we cannot measure, only using floodlights. For example, in a case of weighted conversions, we can give more weight to the one with the most value with offline conversions. In this case, we can unify online and offline conversions under the same algorithm, giving them the same value.

Each of the cases is unique and will vary depending on the weighting we give to each step, action or sales value.  

Our current digital realm requires marketers to personalise their campaigns to ensure that they meet the needs of their customers. Custom bidding provides flexibility and is the right tool to assist marketers in customising their bidding strategies and improving the performance of their campaigns. 

If you want to know more about Custom bidding or DV360 and the Google Marketing Platform tech stack, email us at info@makingscience.com.

Combine your business insights with the Google algorithms and get the most out of your campaigns. ?

In our next Growing with Google session, we will be delving into the benefits of custom bidding and how this solution removes the hassle of manually setting your own bid prices.

Sign up and join Alberto González Fernández, Senior Adtech Consultant, on the 12th of April at 3 PM (GMT).

We welcome the latest version of Google Analytics!

 Google Analytics 4 , Google’s new type of property (yes, there are no views as we are used to) provides, among other things: much more expanded predictive information, deeper integration with Google Ads, more measurement capabilities between devices, and more specific data controls.

The launch is an expansion and renaming of the well-known App + Web property launched last year and marks an important first step in the evolution of Universal Analytics.

We are going to analyse some of its improvements:

• On the one hand, it will provide us with new knowledge and predictions based on artificial intelligence. While knowledge-based machine learning Analytics has been available for some time, new knowledge and predictions that reach Google Analytics 4 can automatically alert the marketers of trends, such as the growing demand for a product. It will also predict results such as churn rates and potential revenue a business could earn from a specific customer segment. All this information can help us anticipate our clients’ actions in the future and thus focus on higher-value audiences.

• Improved integration with Google Ads. Now we can generate and maintain audiences of our users on the web and in the application. For example, if a user is qualified for an audience list due to an action performed on the web and were removed from a list because they made an in-app purchase, the list would automatically update to remove the user and not be impacted with ads. In addition, Google Analytics 4 will report on the actions of the YouTube involved views that occur both in the application and on the web.

• One of the most notable differences between Analytics 4 and Universal Analytics is the organization of the reports based on the customer lifecycle. These reports will help us delve into specific aspects of the customer journey. For example, we can see which channels generate new customers in the user acquisition report and then use the engagement and retention reports to understand the actions these same customers take and whether or not they stay after conversion. The new Analytics experience also aims to offer us a complete view of how customers interact with your business across devices and channels. We can also provide your user ID or enable Google signals to deduplicate users across devices, both in reporting and ad targeting.

• Another benefit is event tracking without code implementation. This makes it easier for us to monitor and measure the important actions on the site, in the App, and in real-time (in Universal Analytics, events usually arrive with some latency). For example, we can measure a scroll event or the playback of a video without adding code or configuring event tracking in GTM.

• We are also going to have a more specific user data control. Analytics 4 also includes options to help advertisers comply with data regulations like GDPR and CCPA. Consent mode is designed for sites that must have end-user consent to collect analytics data, and this new mode provides us with separate consent options for analytics and ads. Data deletion options have also been improved, allowing companies to fulfil user deletion requests without having to delete more data than is necessary. In addition, it includes a preview mode so that companies can verify the data they are about to delete.

• How will it affect our analytics in the future without cookies? Well, as third-party cookies are removed, Google already anticipates that data scarcity will become the new norm. It will rely on machine learning to repopulate data gaps.

In conclusion, the new property of Google Analytics 4 provides a complete cross-channel view of the customer life cycle and uses that information with predictive marketing functions, providing us with more information and more efficient ways to act based on those discoveries.

We recommend that you configure a new property with this new version when you have to configure a new property. The previous one, Universal Analytics, will continue to be available, but we recommend activating GA4 since it will focus on improvements and news within Google and keep both measurements in parallel.

Our team of experts here at Making Science can help you accelerate digitally! Ask for information at info@makingscience.com.

Have you ever reported on conversion tracker by Floodlight, Google Ads conversion tracking and Google Analytics and the total from these different systems did not match? We will explain the important components you should pay attention to when it comes to conversion tracking systems and the difference between the way these three systems measure conversions.

Analysing and visualising the interaction between your website or app users and your marketing tactics before they convert is essential to gather insights and choose the best decision to optimise the conversion rate.

Digital advertising has given us the possibility to reach people with timely and relevant messages, measuring conversions allows us to understand how effective these ads are from awareness to conversion. Previously we would rely on cookies to successfully measure our campaign, these cookies would bring us information about the interaction after a user clicked on an ad. However, since browsers have restricted the use of cookies, scenarios where it’s no longer possible to observe these conversions are increasing. 

The Digital Landscape is becoming complex with more and more gaps in media measurement, the most effective solution is conversion modelling. That means that machine learning will be used to quantify the impact of marketing efforts when the conversion cannot be tracked, so we will depend more and more on IA to make business decisions.

When measuring conversions, these are the initial key points you must consider :

1. Make sure that you are measuring apples with apples, confirm that Floodlight, Google Ads, and Google Analytics columns are the same events. Sounds basic but believe me, it happens. 
2. Confirm that your advertiser’s web server or redirection service hasn’t removed the gclid and gclsrc parameters from the landing page URL.
   When auto-tagging is enabled in Google Ads or your Google Analytics Account is linked with Search Ads 360, these parameters are automatically added. If the parameters are removed, the attribution of the visits to your search campaign will not be possible to measure and the metrics in GA and SA360 will definitely not match.  
3. Some metrics are supposed to get different results since the conversion systems have differences in terms of measurement. Differences include how the conversion attribute system counts, the lookback window, the time zone and more.

Common measurement differences

Multi-Channel Funnels in Google Analytics are not included since conversions’ attribution is different from the GA reports detailed below.

Time Zone

Systems may use different time zones to attribute conversions.

• Floodlight: The time zone of the Campaign Manager account
• Google Ads: The time zone of the engine account
• Google Analytics: The time zone of Google Analytics view. 

Attribution’s day (Exact moment of Attribution/ Attribution window?)

The attribution’s day is when a conversion is reported to have occurred. Let’s see how each system reports the same conversion.

• Floodlight reports it as the day of conversion
• Google Ads reports it as the day of the click on the Ad
• Google Analytics reports it as the day of the conversion

Lookback Window

The lookback window is defined as the period of time between a customer ad’s clicks and a conversion, such as a purchase, lead generation, and more. The Conversion-tracking systems could have different Lookback windows.

• Floodlight: Can be customised  
• Google Ads: Can be customised between 7 and 90 days 
• Google Analytics: Can be customised, but the default is six months. For GA data in Search Ads, the used Lookback Window is the one set in GA.

Last-click attribution 

• When a search ad is clicked and later a display ad is clicked before a conversion.
  • Floodlight: The conversion credit goes to Display click if the data is on a display ad manager by Google Marketing Platform.   
  • Google Ads: The conversion credit goes to Search click if the data is on a display ad manager by Google Marketing Platform or another system. 
  • Google Analytics: The conversion credit goes to display ads if the data is on a display ad manager by Google Marketing Platform or another system.

• When a search ad is clicked and later an organic search result is clicked before a conversion.
  • Floodlight: The conversion credit goes to the ad in Search Ads 360 
  • Google Ads: The conversion credit goes to the ad in Search Ads 360 
  • Google Analytics: The conversion credit doesn’t go to the ad in Search Ads 360.

All conversions and unique conversions

Some systems count just one conversion per user, while others count every conversion that occurs after a click.

• Floodlight: The way that Campaign Manager counts can be configured
• Google Ads: Use flexible conversion counting
• Google Analytics: Google Analytics Goals counting can be configured 

Scaled conversion metrics

The conversion sometimes cannot be measured directly, because of data regulations and data factors such as the unavailability of cookies when conversions can’t be measured directly. 

• Floodlight: The conversion metrics will be scaled by Search Ads 360
• Google Ads: scales conversion metrics
• Google Analytics: does not scale any conversion metric 

Ad robots and other spam

The way that some systems deal with the spam from robots is different

• Floodlight: Visits from ad robots are excluded
• Google Ads: Clicks from ad robots are excluded and provide a more advanced spam filter
• Google Analytics: visits from ad robots are not excluded.

As you can see, there is a chance that we don’t get the exact same data results in every single platform for our marketing efforts. However, we can minimise the gaps by setting up our platforms correctly. If you have any questions regarding Conversion tracking systems, send us an email at info@makingscience.com.