Over the past few years, data privacy has emerged as a major recurring theme in the ad tech space. Data privacy regulations are increasing in number and becoming more stringent as regulators seek to give consumers back control over their personal data.
Data protection laws have been in force since the mid-2020s, butth A century later, Europe started the ball rolling for the current more digitally focused regulatory era with the introduction of the General Data Protection Regulation (GDPR) in 2018, followed by the California Consumer Privacy Policy in 2020. Act, and a number of other laws were introduced in different parts of the country. world. The European Digital Markets Act, which came into force in 2022, aims to further regulate the data collection and sharing activities of big tech companies such as Apple and Google, but these companies will also join in with their own data privacy initiatives. are doing. In 2021, Apple launched its App Tracking Transparency Framework, which gives Apple users the right to opt out of tracking across the apps and websites they use, while Google did the same as part of its Privacy Sandbox initiative. are planning.
At the same time, regulators are looking to strengthen existing laws. Late last year, for example, the Federal Communications Commission sought to amend the Children's Online Privacy Protection Act (COPPA) to make it harder for technology companies to collect and monetize children's data.
These moves impacted ad tech companies in two ways. First, all companies involved in digital advertising must ensure that they meet all relevant standards regarding how they collect, protect, manage and process consumer data and comply with all relevant laws. It is mandatory to check. Key among these standards is SOC 2 Type 2 certification. IAB TCF membership; IAB OM SDK recertification; IAB Tech Lab standards adoption. CCPA validation; GDPR compliance. Obtained ISO/IEC 27001:2013 certification.
Second, more and more companies are recognizing the power of first-party data and are beginning to leverage it to run more targeted, more personalized campaigns. First-party data is certainly a powerful asset, giving businesses real insight into who their users are, what they're interested in, what they're doing, and what they're buying.
But first-party data alone is only half the story. Most powerful when combined with other data points, the problem is that these data points are often fragmented and reside on different platforms, making it difficult to view them holistically and take action on one dashboard. That's not easy to do. .
This is where machine learning comes into play. When you combine first-party data with machine learning and integrate it with other signals, such as those from mobile measurement partners, you get incredibly detailed information about who your users are and what makes them tick.
Machine learning models can map user behavior over time and across multiple channels and touchpoints to identify emerging trends and changes in user preferences. Combining first-party data with real-time engagement data such as user app requests, ad interactions, anonymized in-app behavior data, and demographic information enables granular segmentation of audiences who share similar opinions. You can create multiple segments of users. Features. Advanced ML technologies such as deep learning can also optimize a user's profile by understanding patterns from datasets such as images and videos.
This data can also be used to create models that analyze the relevance of the data, assign appropriate weights to its importance, and provide a view that predicts which ads will resonate with users. , you can spend your marketing dollars more effectively. These models are also useful for remarketing. Remarketing offers a cost-effective way to reduce churn, increase retention, and maximize lifetime value by targeting a subset of “warm” users who have previously expressed interest in your product. To do.
While creating multiple segments of users, machine learning can create hundreds of highly personalized, contextual creatives optimized for each segment and different geographies using technologies such as dynamic creatives. In some cases, you can create thousands of them quickly and easily. Optimize: Quickly iterate and optimize elements of your ad creative to increase engagement and conversions. Everything is done in compliance with the strictest privacy regulations.
Machine learning also provides the ability to provide analytical insights post-campaign as well as in real-time. This means that optimization is more dynamic. Marketers should consider predictive analytics that incorporates both historical and real-time data to more strategically allocate advertising budgets to channels with the highest potential for ROI.
I believe this combination of first-party data and machine learning can help redress the balance between the open internet and walled gardens when it comes to advertising budgets. According to Trade Desk Intelligence and Canvas8, as of April 2023, U.S. consumers will spend 59% of their time online on the open internet, and just 41% in the walled gardens of Google, Meta, and Amazon. I did. Yet, thanks to walled garden targeting, optimization, and reporting tools, walled gardens attract 52% of ad spend compared to just 48% on the open internet. As the open internet, through the widespread use of first-party data and machine learning, can offer the same level of targeting, personalization, accountability, and measurability as walled gardens, more ad spending will move in that direction. , which can only be a good thing. For those who believe in the power and value of an open, ad-funded internet.
Based in Silicon Valley, Greg has provided companies with valuable digital advertising expertise for over 20 years. Throughout his illustrious career, Greg has honed his acumen in the field of digital advertising, holding key roles at top organizations such as Yahoo and his AOL. Prior to joining Mobvista, he oversaw his programmatic exchange at Amobee and spearheaded his programmatic partnership at Celtra.
