Marketers constantly face the challenge of signal loss as we transition to a cookieless environment. Here’s how your advertising can avoid the pitfall of signal loss.
The shift towards a cookieless landscape has led to the worldwide adoption of behaviors that prioritize privacy and information safety. These include the increased use of DNT (do not track) signals, NAI (Network Advertising Initiative) consumer opt-outs, cache clearing, and ad blockers – to name just a few.
In addition, new data and privacy regulations such as the CCPA and GDPR have limited the sharing of personal identifiable information. At the same time, tech giants such as Apple have made iOS updates to make tracking and targeting users more difficult and Google has initiated its plan to phase out third-party cookies in 2024.
As a result, marketers worldwide are left with a plethora of challenges to adapt and transition to cookieless marketing. One of those challenges: signal loss.
Signal loss isn’t new to marketers. However, it’s an increasing concern for brands everywhere as the privacy landscape continuously evolves. In fact, 90% of ad buyers are shifting personalization tactics as a result of increased privacy legislation and signal loss. In turn, ad budgets are increasingly allocated to channels that can leverage first-party data, such as CTV, retail media, and social media.
Moreover, Deloitte Digital found that companies across industries are at risk of losing an average of $91 million to $203 million in revenue per year due to signal loss and impact on advertising effectiveness.
Because traditional digital advertising strategies are not as effective as they once were, businesses must now pivot and find ways to overcome signal loss.
Signal loss, defined
In a nutshell, signal loss is the reduced access to consumer data that powers targeted advertising to its consumers. It refers to the diminishing strength of targeted marketing and data collection on important signals or data points (such as purchase events) which were once reliably tied to campaigns and individuals.
This was possible due to third-party cookies and device identifiers that enabled marketers to identify and target individual leads and prospects online (as well as access highly valuable data points like prior purchases, personal preferences, and location insights used during prospecting).
This loss of signal impairs customer acquisition and audience reach while shrinking returns on ad spending and increasing customer acquisition costs. It also poses significant challenges for marketers when it comes to personalization, look-alike modelling, audience targeting and retargeting, campaign reporting, and marketing attribution.
Ultimately, users have more control over how their data is used and stored by platforms and applications. This is important as users are increasingly wary of how their personal information and browsing data are stored and used, emphasizing their need for data privacy and transparency.
As a result, brands must now identify a reliable and sustainable data source for targeted marketing going forward.
Tips on preventing signal loss
Let’s face it: signal loss in marketing is something all brands will experience at some point. But that doesn’t mean it is the end of the world. Here are some tips to overcome and mitigate signal deprecation:
- Invest in privacy-conscious targeting and measurement: Consider alternative solutions to uphold commitment towards keeping customers’ data private. Conduct brand lift studies and incrementality tests to gauge the incremental impact of your ads and media tactics.
- Increase access and reliance on first-party data: First-party data and information collected directly from consumers can be valuable in creating detailed customer profiles and understanding consumer preferences. Additionally, advertisers can use this information and segment their target audiences accordingly. As a result, they can deliver personalized marketing that resonates with their target audience.
- Consider contextual advertising: Contextual advertising involves placing ads on websites based on their content and relevance to the user’s interests. It doesn’t rely on tracking individual user behavior, making it a privacy-friendly alternative to behavioral targeting while staying relevant and engaging to the user’s experience.
- Make use of identity graphs and other predictive models: It’s worth considering adopting innovative analytical and predictive methods that are less dependent on tracking signals and third-party cookies. Machine learning, AI, media mix modelling, lookalike modelling, and identity graphs can be good alternatives. Identity graphs use AI to group identifiers into individuals and households to create profiles. These profiles can then target users without gathering unconsented personal information.
Overcome signal loss with illumin
In addition to the tips mentioned above, there are various tools and platforms marketers can use to overcome signal loss and step up their cookieless advertising game.
illumin leverages a multi-prong approach to identifying consumers based on multiple provider signals through an AI-driven illumin Identity Graph (ilG). The iIG is an intrinsically cross-device graph rooted in an identifier called the ‘AIID’. As a new signal arrives and older signals decay, vertices and edges are introduced into a weighted graph that is then regularly analyzed for relationships, fraud, and unique identity.
Deterministic identities like MAIDs and RampID can be given more weight while probabilistic IDs less. Graph data is then surfaced to help with user identification in the illumin bidding tiers. The graph and bidding tier data are regularly refreshed in real-time.
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