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As the cookie crumbles: How to look at various strategies for personalized experiences

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The digital marketing landscape is undergoing a transformation that is unraveling past operating principles, especially for the Retail and Consumer Packaged Goods (CPG) industries, as well as other B2C companies. Established operating pillars reliant on third-party data to advance personalized advertising efforts is no longer sustainable with privacy concerns on the rise and increasing regulatory pressure.

Google’s slow yet apparent move to phase out third-party cookies in Chrome (representing approximately 65% of the global browser market), or similarly, the deprecation of mobile advertising identifiers (MAIDs), is forcing brands to rethink. Furthermore, regulations like GDPR and CCPA are setting stricter rules on how consumer data can be collected and utilized.

Does this seismic shift signal the doom of personalization strategies for advertisers? We don’t think so. Instead, it presents a unique opportunity for companies to create diversified strategies that grow their first-party data and leverage AI to discover user intent in new ways. Let’s take a closer look at how.

Emerging practices in a privacy-first world

In providing an avenue for advertisers to deliver relevant, personalized experiences while staying compliant with privacy standards, the following four strategies show opportunity and emerge at the top:

  1. Consent-based first-party data strategy

    As privacy regulations become more stringent and third-party cookies fade into the past, first-party data emerges as the cornerstone of personalized marketing. First-party data refers to the information a brand collects directly from its customers through various touchpoints—such as website visits, app interactions, social media engagement, and customer surveys. Since this data is voluntarily shared by users, it is not only more reliable but also more privacy-compliant.

    The key advantage of using first-party data is that it provides high levels of identify confidence, meaning marketers can confidently deliver personalized messaging. Additionally, brands can build strong feedback loops—where customer actions continuously inform and refine targeting strategies. This is particularly useful for high-value customers who regularly interact with the brand.

  2. Aggregator walled gardens

    Despite the shift toward privacy-compliant strategies, aggregator-walled gardens—such as Google, Facebook, and Amazon—remain powerful players in the world of digital advertising. These platforms have vast amounts of user data that can be leveraged for precise targeting, especially when combined with tools like Google Ads Data Hub, Facebook’s Advanced Analytics, and Amazon’s Clean Rooms. Ultimately, it provides brands with access to aggregated, anonymized data that can be used to create highly refined audience segments.

    Moreover, walled gardens offer several advantages, including solid data practices and high match rates—meaning that the data they provide for audience segmentation is reliable. Additionally, since these platforms cover a large swath of users across multiple devices and screens, they allow companies to reach their target audiences more effectively.

  3. Google Privacy Sandbox and Apple Private Click Measurement (PCM)

    In response to the phasing out of third-party cookies, both Google and Apple have introduced new initiatives that allow brands to target users in a privacy-compliant manner without relying on traditional identifiers. Google’s Privacy Sandbox aims to provide an alternative to third-party cookies by focusing on aggregated data and known audience segments. This initiative allows advertisers to target groups based on broad categories, such as interests, without tracking individual user behaviors across websites. Similarly, Apple’s PCM enables privacy-preserving ad measurement, which supports click and conversion tracking without using cookies or personal identifiers.

    Both Google and Apple’s solutions offer the ability to maintain traditional targeting practices like retargeting, frequency capping, and even attribution analysis, albeit using proprietary models that don’t rely on direct user identification.

  4. Contextual targeting

    As the use of personal identifiers becomes more restricted, contextual targeting is gaining renewed attention. This strategy focuses on delivering ads based on the content of a webpage, app, or platform rather than user behavior or demographics.

    Contextual targeting offers the advantage of being privacy-compliant, as it doesn’t require access to personal data or cookies. It can also cover a large audience, regardless of their personal identifiers or browsing history. Additionally, as contextual targeting doesn’t depend on individual user data, it works effectively across a wide range of environments.

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Key recommendations and real-world examples

For Retail, CPG, and B2C brands looking to future-proof their personalization strategies, the following recommendations become imperative:

  • Develop strong first-party data strategies

    Building a robust first-party data strategy is now a cornerstone for brands looking to maintain personalized marketing efforts. Focus on collecting data directly from your customers through interactions on brand-owned channels and be transparent with your users about how their data will be used, ensuring consent is properly obtained. Further, implementing a Customer Data Platform (CDP) will allow you to centralize this data, creating detailed, actionable customer profiles for more precise targeting.

    Take, for instance, this leading women’s apparel company that was facing a challenge in targeting and personalization due to lower match rates on platforms like Facebook. However, by harnessing the power of first-party data across key strategies of audience segmentation, extended retargeting, and exclusions, the company enhanced its cross-selling and upsell opportunities to niche audience groups.

  • Cultivate relationships with walled gardens and experiment with data clean rooms

    Platforms like Google, Facebook, and Amazon still offer significant reach and data insights. To take advantage of their targeting capabilities, brands should build strong relationships with these platforms and experiment with data clean rooms. These environments allow brands to use aggregated platform data in a privacy-compliant way, enabling more refined audience segmentation and attribution without exposing individual user data.

    For example, a major US-based media company launched a data clean room that allowed advertisers to merge their first-party data with the company’s own audience insights while protecting personally identifiable information. This platform offered key functionalities, including discovering customer overlaps for improved targeting, providing ad exposure data to prevent excessive targeting with frequency capping, and enabling cross-platform attribution to optimize campaign performance.

  • Implement a graph-based fluid identity framework

    With identifiers becoming increasingly scarce, brands should focus on building a graph-based identity framework that allows for the flexible resolution of user identities across multiple touchpoints and devices. This approach enables to track customer behavior and maintain personalized interactions without relying on cookies or other third-party identifiers.

    Illustrating this is a high-end electronics brand that faced fragmented customer identities across multiple touchpoints, making personalized targeting difficult. By adopting a graph-based fluid identity framework, the brand connected diverse identifiers (email, device ID, in-store data) into unified, real-time customer profiles. This approach enabled accurate, cross-channel personalization and dynamic updates to customer data.

Way forward

As companies untangle a world of privacy-first solutions, leveraging a mix of the aforementioned strategies will be crucial in maintaining personalized, effective advertising campaigns. Whether focusing on first-party data, experimenting with walled gardens, adopting new privacy-preserving technologies, or embracing contextual relevance, the key is to adapt, diversify, and stay aligned with privacy standards to foster trust and engagement with today’s empowered consumers.

HTCNXT is working with brands to develop implementations that drive all these strategies. Backed by strong advisory across processes, a robust partner ecosystem, and proven experience in disruptive technology domains, HTCNXT offers the expertise, tools, and insights needed to future-proof your digital advertising approach in a privacy-first era.

AUTHOR
SME Retail Expert Shammo Ghosh

Shammo Ghosh

Senior Vice President, SME Retail

SUBJECT TAGS

#ArtificialIntelligence

#RetailMarketing

#AIinMarketing

#CustomerExperience

#DigitalMarketing

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