Signal loss has become a critical obstacle for marketers, disrupting their ability to measure and optimize campaigns efficiently.

To address this disruption, advertisers must first understand the true impact of the problem, the challenges it poses, and effective strategies for moving forward in a landscape reshaped by privacy regulations and technological shifts.

Understanding Signal Loss and Its Implications for Advertisers

Signal loss denotes the diminishing presence of user-level data identifiers, a foundational element in performance-driven marketing. Regulatory actions and privacy initiatives by major tech players such as Apple and Google have been instrumental in driving this transformation. Initiatives like Apple’s App Tracking Transparency (ATT) framework and Google’s forthcoming deprecation of the Google Advertising ID (GAID) are key drivers of these changes. The phase-out of third-party cookies by browsers like Firefox and Safari, further complicates the scenario.

While prioritizing user data protection is pivotal, these measures have posed challenges for performance marketers heavily reliant on user-level data for targeting, measurement, and optimization. The main obstacles encountered include the fragmentation of data and the constraints of limited, delayed data availability.

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Challenges Faced: Fragmented Data and Limited, Delayed Signals

1. Fragmented Data:

Marketers are now inundated with campaign data from varied sources in multiple formats, exacerbating complexities in assessing campaign performance post iOS 14.5’s release. Data stemming from sources like Mobile Measurement Partner (MMP) attribution, OS stores, and walled garden attribution contributes to this fragmented landscape, hindering optimization and scalability due to the absence of standardization.

2. Limited and Delayed Data:

Unlike the comprehensive data sets facilitated by IDFA, current alternatives like SKAN offer delayed signals and restricted insights. The lack of real-time signals for optimization is a notable hurdle marketers must address.

Strategies to Overcome Signal Loss

Despite these challenges, marketers can adapt by capitalizing on available data signals, aggregating disparate sources into a coherent overview. As cross-company data sharing experiences restrictions, the value of third party data diminishes, urging marketers to lean into first- party data — an invaluable resource that requires prioritized collection and utilization. Leveraging new signals from first- party data empowers marketers to elevate their User Acquisition (UA) and re-engagement campaigns significantly.

Moreover, through utilizing a data collaboration platform built on data clean room technology, companies can establish a trustworthy environment for monetizing first-party data, activating audiences, and conducting lift measurement studies in adherence to privacy protocols. By facilitating the secure sharing of first-party user level data across different entities, media networks can unlock the full potential of their prized data assets.

Furthermore, the evolution of Commerce Media Networks emerging as a data refuge amidst signal loss challenges opens new doors for marketers. Leveraging first-party signals via Commerce Media Networks offers a fresh advertising avenue that harnesses rich consumer data from retailers, travel platforms and commerce intermediaries, and aligns with privacy mandates while connecting with intent-driven audiences.

Empowered by Commerce Media, marketers can harness the vast potential of first-party data and a diverse array of formats and channels to target full-funnel objectives while streamlining closed-loop measurements.

As marketers navigate these complexities, employing cutting-edge strategies and embracing new data signals will enhance their resilience in the face of signal loss challenges. Taking advantage of strategies such as lookalike modeling, AI-driven insights, enriched engagement tactics, and cross-platform attribution, while also establishing a unified data source in a privacy-preserving manner, offers a roadmap to reinforce measurement confidence and optimization strategies. The era of data collaboration beckons, propelling marketers to thrive amid the transformative challenges of signal loss.

Navigating Signal Loss: The New Era of Data Collaboration

In the age of privacy, signal loss has emerged as a significant challenge for marketers, jeopardizing their ability to target, measure, and optimize campaigns. Despite this, innovation and adaptation offer a path forward by building a new data reality with both existing and new signals.

Understanding Signal Loss and its Impact on Advertisers

Signal loss refers to the reduction of user-level data identifiers, which have traditionally underpinned performance-driven marketing. This change has been driven by increased regulatory scrutiny and privacy measures from tech giants like Apple and Google. Apple’s App Tracking Transparency (ATT) framework and Google’s planned deprecation of the Google Advertising ID (GAID) are major contributors to this shift. Additionally, the phasing out of third-party cookies by browsers like Firefox, Safari, and soon, Chrome, further complicates the landscape.

While these privacy measures are crucial for protecting user data, they have made it difficult for performance marketers who rely on user-level data for targeting, measurement, and optimization. The main challenges include fragmented data and limited, delayed data”

1. Fragmented Data:

Marketers now receive campaign data in many different forms and from various sources. This fragmentation, exacerbated since the release of iOS 14.5, complicates the understanding of campaign performance. Data comes from multiple sources like MMP attribution, OS stores, and SRN attribution, making it difficult to find success and drive scale without standardization.

2. Limited and Delayed Data:

Compared to the full data granularity for users with an IDFA, current alternatives like SKAN offer delayed signals and limited values. This is a major challenge as marketers are used to real-time signals for optimization.

Overcoming Signal Loss: Strategies for Marketers

Despite these challenges, marketers can adapt by leveraging every available data signal and consolidating disparate sources into a unified view. With data sharing between different companies significantly limited, the value of 3rd party data drops considerably. Instead, marketers must step up their use of 1st party data, which in any case is the most valuable source of data at their fingertips. As part of a robust 1st party data strategy, marketers should prioritize its collection and utilization. With more new signals from 1st party data, marketers can take their UA and especially re-engagement campaigns to the next level.

But this data need not remain only within a company’s own environment. A data collaboration platform can then serve as a trusted environment for 1st-party data monetization, audience activation, and measurement. By allowing different companies to share 1st party user level data in a privacy-compliant way, marketers can truly maximize the potential of their own highly valuable data

Data Collaboration Platforms  fuels hypergrowth between retail/commerce media networks and brands through a trusted environment for first-party data monetization and audience activation, as well as built-in measurement capabilities to drive continued growth.

Commerce Media Networks: A Safehaven to combat Signal loss

Signal loss that has driven the desire for First party Commerce media is an entirely new advertising channel that offers the benefit of utilizing rich 1st-party consumer data from retailers in a privacy-compliant manner, a viewership with high purchase intent (a.k.a. shoppers), and a direct connection to the actual end transaction.

Commerce media incorporates elements from all the categories of retail media to meet full-funnel objectives using large-scale 1st-party data and a wide range of formats/channels (from video and CTV to contextual and sponsored product ads) — all while enabling closed-loop measurement.

Data Collaboration Platform combats these challenges by enabling privacy-compliant data collaboration and providing advanced measurement capabilities. By leveraging first-party data effectively, marketers can drive business growth, improve products, and enhance customer experiences.

The shift towards greater privacy and data protection has undeniably complicated the digital marketing landscape. However, by adopting innovative strategies and leveraging new data signals, marketers can navigate the challenges of signal loss. Embracing modeling, AI-driven creative insights, enriched engagement types, first-party data, cross-platform attribution, and a single source of truth will enable marketers to maintain confidence in their measurement and optimization efforts. The era of data collaboration is here, and with the right tools and approaches, marketers can continue to thrive despite the evolving challenges of signal loss.

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