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December 2020 Updates to Paid Media Platforms



In this monthly post, we bring you the latest from all of the major platforms.

Google Ads

What: Google Ads Editor v1.5 has been released

Details: The new version of Editor provides a large list of product updates including filter adjustments, image extensions, updated statistic columns, detailed recommendations, ad strength review, and more.  

Impact: For advertisers using any of these features, functionality has improved.

Impact: For advertisers using any of these features, functionality has improved.

What: The campaign goal metric has shifted locations

Details:  The campaign goal metric is now found near your optimization score making it easier to understand how recommendations are prioritized.

Impact: Google’s Optimization Score provides a quick, automated second set of eyes to identify major gap areas. Connecting Campaign Goal to these recommendations makes them more likely to provide action ready insights. 

What: App campaigns for engagement now available globally

Details:  Although introduced more than a year ago, App campaigns for engagement are now available for all eligible advertisers. Apps must have an audience of at least 250,000 installs. Additionally, App Engagement campaigns must be set up under a separate account from the App Install campaigns.

Impact: Google’s rollout provides an avenue for apps to nurture their existing user base instead of focusing solely on new user acquisition on the platform.

What: Discovery ads can now use social assets

Details: Discovery ads now support 4:5 portrait aspect ratio layout.

Impact: Google continues to expand the use cases of Discovery Ads. Back in August, the trend was pushed forward by incorporation of lead form extensions.

What: Explanations expands to Target CPA campaigns on Search

Details: Explanations provide reasons for a shift in performance. These are now available for Search campaigns using Target CPA.

Impact: Google increases its offerings of automated insights to help advertisers. As always, these updates should be utilized as a spot-check/second set of eyes to supplement traditional campaign analysis and individual account context. 

What: View estimated sizes of similar audiences

Details: Previously Google only showed sizes of similar audiences when applying them to campaigns. Now such information is easily found in the Audience Manager.

Impact: Advertisers looking to monitor or bid via Similar Audience have streamlined access to relevant information such as expected reach.

Microsoft Advertising (Bing)

What: Data retention increase on UET

Details: Universal tracking data can now be retained for 390 days (13 months) which is an increase from 180 days. 

Impact: For many advertisers, being able to remarket to a segment that goes back >1 year is valuable ( ex ] sports retailer selling to returning customers at the start of each new season). The increase in data retention provides long term flexibility for these audiences. 

What: Share of Voice now available for the Microsoft Audience Network

Details: Share of voice data reporting is now available for Microsoft Audience Network.

Impact: Previously advertisers lacked share of voice data when viewing Microsoft Audience Network. The expansion of this key performance indicators helps inform account strategy in regards to auction competition and strength of bids.

What: Reporting and budgeting updates for search campaigns

Details: The platform has added custom columns to create individualized reporting metrics. Additionally, Microsoft has refined the notification experience for high certain scenarios such as campaigns running out of budget or an expiring insertion order. 

Impact: Most accounts are likely accustomed to Custom Columns already within Google Ads. Advertisers will be excited to see Microsoft follow that lead yielding additional reporting flexibility within the interface.

General Note

If you missed last month’s update, turn to November 2020 Updates to Paid Media Platforms for all of the latest info. 

Did we miss any major monthly updates? Not covering a certain platform close enough? Feel free to let me know on Twitter @Will_Larcom


10 Ways Machine Learning Can Transform Your Business



10 Ways Machine Learning Can Transform Your Business

Many top companies are already using machine learning to target potential customers, personalize offers, and improve customer service, and it will only become more critical in the years to come. So as a business, you need to start understanding how machine learning can be used to benefit your company.

If you’re unfamiliar with machine learning, it’s a form of artificial intelligence that allows computers to learn and improve from experience. Machine learning is used in various ways, such as identifying patterns in data, making predictions based on data, and improving decision-making.

Personalize Your Marketing Messages to Each Individual Customer

One of the most common ways businesses use machine learning is to personalize marketing messages to each customer. By using data about a customer’s previous interactions, purchases, and browsing behavior, companies can send highly personalized messages that are much more likely to resonate with the customer. Not only does this lead to more engaged customers, but it also leads to more sales.

The customer experience is getting more personalized with the use of quick-to-make QR codes. For example, some tools harness data and machine learning to drive a more engaging visible-digital campaign for users who scan product QR codes. It allows each individual shopper not just one but many paths depending on their profile information as well as previous preferences.

Track Customer Interactions With Your Marketing Content and Measure the Impact of Each Interaction

Another way machine learning can be used to benefit businesses is by tracking customer interactions with marketing content and measuring the impact of each interaction. This allows companies to see the most effective marketing content and make changes accordingly.

This is especially important in the age of digital marketing, where there are seemingly endless marketing channels and content options. By using machine learning to track customer interactions, businesses can focus their efforts on the marketing channels and content that are most effective.

Identify Which Marketing Channels Are Most Effective for Reaching Your Target Audience

Similarly, machine learning can also be used to identify which marketing channels are most effective for reaching your target audience. This is important because it allows businesses to focus their marketing efforts on the channels that are most likely to reach their target customers.

When using machine learning to identify effective marketing channels, several factors can be considered, such as customer demographics, customer behavior, and past interactions. By considering all of these factors, businesses can develop a more targeted and effective marketing strategy.

Optimize Your Marketing Campaigns for Maximum ROI

Additionally, machine learning can also be used to optimize your marketing campaigns for maximum ROI. Businesses can use data from past campaigns to identify which campaign elements are most effective and make changes accordingly.

This is especially important for businesses looking to get the most out of their marketing budget. That’s because companies can reduce their marketing spending by using machine learning to optimize their campaigns while still achieving their desired results.

Predict Customer Behavior and Target Your Marketing Efforts Accordingly

Machine learning can also be used to predict customer behavior. This is important because it allows businesses to target their marketing efforts accordingly.

For example, if a business knows that a customer is likely to purchase a product in the next month, it can target its marketing efforts to that customer. This is a much more effective use of marketing resources than targeting customers who are not likely to purchase anything in the next month.

Identify and Analyze New Marketing Opportunities

One of the most important things businesses can do with machine learning is to identify and analyze new marketing opportunities. Using data, companies can find recent trends and target their marketing efforts accordingly.

This is especially important in today’s fast-paced world, where new opportunities can pop up anytime. By using machine learning, businesses can be one of the first to take advantage of new opportunities and stay ahead of their competition.

Assess the Effectiveness of Your Current Marketing Strategies

Current marketing strategies can also be assessed with machine learning. This is important because it allows businesses to see which methods are working and which ones need to be changed.

This assessment can be done by looking at data from past marketing campaigns and seeing which ones were most successful. By doing this, businesses can change their current marketing strategies to make them more effective.

Improve the Overall Quality of Your Marketing Content

Not only can machine learning be used to identify which marketing channels are most effective, but it can also be used to improve the overall quality of your marketing content. This is important because it allows businesses to create content that is more likely to reach and engage their target audience.

Several factors can be considered when using machine learning to improve content quality, such as the topic, the tone, and the target audience. By considering all of these factors, businesses can create content that is more likely to be successful.

Detect and Prevent Fraud in Your Marketing Operations

Fraud is a dangerous problem in any business operation, and marketing is no exception. That’s why it’s vital to use machine learning to detect and prevent fraud in your marketing operations.

There are several ways to do this, but one of the most effective is using data from past campaigns to identify fraud patterns. By doing this, businesses can be more proactive in their fight against fraud and keep their marketing operations safe.

According to Ben Michael, Practicing Lawyer and Founder of Michael & Associates, “Fraud is the number one enemy of all marketers. It not only wastes their time and resources, but it also damages their reputation. That’s why they need to be proactive in their fight against it. Machine learning is the perfect tool to help them do that.”

Make Better, More Informed Decisions About Your Marketing Strategy

Finally, machine learning can make better, more informed decisions about your marketing strategy. This is important because it allows businesses to understand better their target market and what kinds of marketing strategies are most effective.

This can be done using data to create models predicting customer behavior. By doing this, businesses can make decisions about their marketing strategy based on data rather than guesswork. This helps companies make the best possible decisions about their marketing efforts.


Machine learning is a powerful tool that can improve marketing efforts in several ways. By using machine learning, businesses can identify new marketing opportunities, assess the effectiveness of current marketing strategies, improve the quality of their marketing content, detect and prevent fraud, and make better, more informed decisions about their marketing strategy.

If you want to improve your marketing efforts, consider using machine learning. It can help you take your marketing to the next level!

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