At Adlook, we understand the critical importance of frequency capping in the advertising technology, particularly as the industry shifts away from reliance on third-party cookies. As a company deeply ingrained in developing branding solutions, it is crucial for us to deliver effective frequency capping mechanisms that maintain campaign effectiveness and user engagement in a cookieless environment.
Frequency capping limits the number of times a specific advertisement is shown to the same user over a designated period. This technique is essential in preventing ad fatigue, where users may become disinterested or annoyed by repeated exposure to the same advertisement. Historically, this has been managed using cookies or digital identifiers to track ad impressions per user, which ad servers use to ensure ads are not served beyond the predetermined threshold.
The process involves setting a cap on the number of times an advertisement is shown to an individual during a specified period, such as per day, week, or month. Effective frequency capping enhances the user experience by ensuring that the exposure to ads is balanced, thus maintaining the campaign’s overall efficiency without oversaturating the audience.
For branding campaigns, setting an optimal frequency cap requires a strategic balance that considers several factors:
The adtech environment is shifting towards cookieless. In July 2024, Google changed their approach to removing third-party cookies from the planned phase-out to giving users controls in their browser over 3rd party cookie usage. These kinds of user decisions are influenced by design: What will be the defaults? How easy will it be to make data-sharing changes? Google can follow Apple's approach and phase out cookies relatively quickly as people update to newer versions of Chrome. In this situation, this would mark a shift in approaches from a structured decision, highly influenced by industry and government institutions, towards a process in which only Google is closely communicating with users. With this development advertiser’s readiness for a cookieless environment needs to remain top of line as it is more important than ever.
As of July 2024, approximately 24% of users were using browsers that block third-party cookies by default. The majority of this share comes from Safari users. Advertisers need to pivot toward innovative frequency capping strategies that don’t rely on traditional cookies. Utilizing probabilistic modeling and first-party data has become paramount, allowing for a more refined approximation of user sessions based on anonymized identifiers such as device type and operating system. Contextual targeting has also gained prominence, aligning ads with content to ensure relevancy and effectiveness, without the need for past user behavior analytics.
To create a comprehensive frequency capping strategy in a cookieless environment, advertisers can integrate multiple signals:
Publisher IDs and external IDs: Combining publisher IDs with external IDs can enhance frequency capping across different publishers and external platforms. This integration ensures consistent ad exposure levels, avoiding redundancy and enhancing user experience by coordinating caps across various media channels. It provides good frequency capping capabilities within the domain or publisher-owned inventory – however, with this method, it’s not possible to apply capping cross-domain. It relies on publisher data that isn’t available everywhere.
Short time intervals and domain-based Frequency: Integrating time-based caps with domain-specific limits allows advertisers to control how often ads are shown within certain time frames and on specific domains. This dual approach helps tailor the advertising efforts to fit the context and timing, which is crucial for campaigns targeting specific industries or during particular events. Usability in such cases is very limited, though, as it’s impossible to precisely select and measure frequency, especially in longer time frames.
Real-time data access and adjustment: With the Shared Storage API, advertisers can access real-time data on ad exposures, allowing for immediate adjustments based on combined signal inputs. If a frequency cap is reached on one campaign, the system can automatically redistribute impressions to other buyers, or adjust the campaign's focus, optimizing overall ad spend and impact. Shared storage-based solutions can be an issue for smaller companies, which do not always have enough campaigns using similar targeting. In a situation where all participating campaigns reached frequency thresholds, it will increase ad waste for the technology. This will result in not having any campaign to show for the impression that is already bought.
At Adlook, we are leveraging AI and data science to enhance our frequency capping capabilities. By employing machine learning models that analyze vast data sets, we can predict user behavior and adjust ad frequencies in real time. Adlook has tested AI-driven methods on 10 large publishers, comparing publisher-specific IDs for frequency capping. The tested approach is showing promising results, closely approximating the effectiveness of third-party cookies. Campaigns we’ve run using alternative frequency capping methods, compared to using third-party cookies as a reference, showed that 97% of users exposed to the campaign were within the defined frequency cap.
Our commitment to innovation and ongoing experiments and testing of AI-enhanced models underscores our commitment to adapting and thriving in a cookieless environment. Adlook is dedicated to ensuring that, even as traditional tracking methods become obsolete, our branding solutions remain robust and highly effective, achieving precise frequency capping to optimize both user engagement and campaign success.
