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The Tracking Prevention Revolution

As consumers look for more privacy, they are increasingly enabled by technology to escape tracking. Since 2017, all the main web browsers have put in place or announced initiatives to make online user tracking more difficult and transparent. Although the specific policies of these initiatives - commonly referred to as tracking prevention – vary between browsers, they all undermine the use of cookies for marketing purposes.

 

Third-party cookies, tracking codes used for targeting audiences across websites, are particularly challenged by tracking prevention. For brands, it means a wide array of marketing techniques, such as remarketing or multi-touch attribution, are affected.

 

 

 

Not all organisations have been impacted in the same manner by tracking prevention so far. Factors such as in-market penetration of each browser and the sophistication of each brand’s digital marketing strategy all play a role in how companies have been absorbing the tracking prevention shock. According to our 2020 Global Client Survey, about one marketer out of four (23%) declares that tracking prevention has already had an impact on their marketing to this point. One in ten (11%) declares no impact. More worryingly, 60% declare they are not familiar with tracking prevention or are unsure about the consequences on their business, showing that this fast-changing landscape is not completely understood by everyone.

 

There are two simple truths for brands to remember.

 

First, collecting user-explicit consent is and will remain paramount to build long-term data strategies. Depending on the market wherein you operate, it can be a matter of compliance with data regulation. It is also a matter of transparency and trust between your organisation and the users of your services.

Second, third-party cookies will be obsolete by 2022. Therefore, the clock is ticking to reassess the data strategies powering their marketing efforts. In light of this privacy targeting shift, marketers can have the most confidence in the long-term usability of their own first-party data, as well as data leveraged in partnership within closed ecosystems.

 

 

Building a first-party data infrastructure

 

This approach aims to shore up your first-party data (i.e., data that you collect directly through loyalty logins, in-store signups, lead forms, etc.) in place of existing cookie-based programmes. By building and cultivating rich sources of user identifiers, companies can get a better understanding of their audiences and leverage one-on-one targeting opportunities, all without relying on third-party cookies.

 

As you develop bespoke segmentation models, you can pass lists of known users between Customer Data Platforms (CDPs) and Demand Side Platforms (DSPs) to target users with personalised creatives across media channels like social and paid search. You can also build lookalike profiles that you can then incentivise to join your loyalty programme in order to grow your first-party data set and CRM.

 

Although this approach can be costly, the deep understanding of customer lifetime value (CLTV) it enables is highly beneficial to brands looking to drive engagement over time. All customers are not equal and should not be valued as such. A robust CLTV perspective can drastically change how brands drive their business. It influences performance indicators and bidding strategies (e.g., the target ROAS is not only based on the revenue from the most recent purchase). It influences predictive modelling and targeting (e.g., building and targeting lookalike profiles with the highest profitability over time). For public companies, CLTV can also impact their stock market valuation, with new analytical companies such as Theta Equity Partners specialising in analysing customer behaviour over time to inform investors. A first-party data infrastructure is therefore a robust foundation for truly understanding the data you currently own before making other structural data-based decisions.

 

Leveraging closed ecosystems

 

In lieu of cookies, a closed ecosystem approach relies on signed-in users of platforms such as Google or Amazon, often referred to as walled gardens. This approach allows brands to upload their own historical audiences in the form of CRM data into the platform. This can be a straight upload, or it can be a more complex integrated solution, for instance, Google BigQuery. Within BigQuery, advertisers can cluster audiences into segments based on behavioural and purchase history (e.g., top spenders), look up similarities across Google's own data, and pick out key similar attributes within those segments (e.g., 40+ men in market for dating services). From there, advertisers activate by applying both CRM and Google owned audiences like lookalike Similar Audiences to existing Google marketing campaigns (e.g., search, YouTube) and optimise visibility, investments and performance according to data.

 

Leaning into closed ecosystems has its own set of benefits. It starts with ease of activation, thanks to integration with the platform’s native activation products. It is especially interesting for brands that are both comfortable sharing their first-party data and already rely heavily on components of those ecosystems. Additionally, this approach is particularly useful for brands with CRM data that are unable to immediately invest in a nurturing program.

 

The trade-offs between the paths to activation

 

Which path is the most advantageous for your brand? A few considerations can help you make an educated choice.

 

 

Although the choices you make now will impact long-term modelling projects and your eventual source of truth, the two paths are not mutually exclusive. For instance, a better handle on your first-party data will enable you to make more informed decisions about what is needed from a closed ecosystem down the line.

 

This article is excerpted from the report Future Focus 2021: Brands Accelerated.

Download it now for key insights on how brands can make the most of brand and performance to accelerate their growth.