Cookies have long been a cornerstone of the digital advertising industry, as they allow advertisers to track users across the web, deliver personalized ads, and measure the effectiveness of their campaigns.
However, in the last five years, popular browsers are increasingly dropping 3rd party cookie support. Following is a timeline of events.
Cookie Deprecation Timeline
What are 3rd Party Cookies and Why is it being Deprecated
Third-party cookies are cookies that are placed on a user’s device by a domain other than the one the user is visiting. They are commonly used by advertisers and other third-party entities to track users across the web and deliver targeted ads.
The deprecation of third-party cookies refers to the fact that many web browsers and other technologies are taking steps to block or limit the use of these cookies. This is being driven by a variety of factors, including concerns about user privacy, the proliferation of ad blockers, and browser updates that block third-party cookies by default.
The Impact of Third Party Cookie Deprecation
The impact of third-party cookie deprecation on the digital advertising industry is likely to be significant. Advertisers rely on third-party cookies to track users across the web, deliver personalized ads, and measure the effectiveness of their campaigns. Without the ability to use third-party cookies, advertisers may have a harder time delivering targeted ads and may see a decline in ad effectiveness and revenue.
However, First Party cookies are not being deprecated and will continue to exist. Hence digital analytics platforms such as Adobe Analytics and Google Analytics will continue to function as is.
The deprecation of third-party cookies will primarily impact the Ad Tech Players.
How to Advertise without Cookies
So what will be the future of digital advertising in a Cookieless world?
So here are a few ways marketers will adopt to deliver digital advertising in a cookieless world.
- Emphasis on First party data: In a cookieless world, advertisers will need to rely more on first-party data to target their advertisements effectively. First-party data is data that is collected directly from the user by the advertiser. Digital Analytics platforms such as Adobe Analytics and Google Analytics rely on first-party cookies to capture user behavior. Also, Optimization and personalization tools like Adobe Target, Google Optimize, etc. also use first-party cookies. This can help the website improve its user experience and identify areas that need to be redesigned or improved. Also first-party data is more privacy-sensitive compared to third-party data, which is collected by third parties and shared with advertisers.
- Zero-party data and progressive profiling: Zero-party data is information that a client willingly and intentionally provides to a company. It can comprise data from gated content pages, purchase intents, personal settings, and information about how a person wants to be known by a brand. This can also be collected via progressive profiling of customers wherein additional questions are asked at relevant touchpoints.
- Identity Solutions: There’s a rise in demand for Privacy-compliant identity solutions that consume data from multiple sources and leverage an identity service to correlate and unify the data that help marketers in delivering personalized experiences to customers. Customer Data platforms (CDPs) leverage identity solutions. Moreover, CDP products like Adobe Experience Platform are consistently gaining traction since it leverages the Adobe ecosystem of products and is able to effectively deliver personalized experiences.
- Walled Gardens and Data clean rooms: A data clean room is software that allows marketers and businesses to match users’ data without actually providing any PII/raw data. Data clean rooms are used by major advertising platforms such as Facebook, Amazon, and Google to offer marketers correlated data on the effectiveness of ads on their platforms. The notion of a data clean room is becoming associated with walled gardens, but with growing constraints, it may not be feasible for firms to examine marketing data only within walled gardens. It is feasible to create a safe and privacy-compliant data environment in a private data clean room, allowing for user-level insights, measurement, and targeting at an advanced level.
- Cohort marketing implies categorizing your target audience into smaller groups (cohorts) that have similar propensities and attributes. Cohort analysis boosts marketing success by determining what connects people who execute similar activities and then planning advertisements appropriately.
- Marketing mix modelling is a strategy that leverages historical performance data to give insights into marketing approaches and helps you identify trends such as events, seasonality, anomalies, brand equity, and more. When granular information regarding the buyer’s journey is unavailable, Marketing mix modelling leverages statistical techniques used to analyze the effectiveness of a company’s marketing strategies. It involves collecting data on various marketing activities, such as advertising, promotions, and pricing, and using statistical models to understand how these activities contribute to sales and other business metrics.
- Google’s Privacy Sandbox is a proposed set of APIs and technologies that are intended to help advertisers continue to deliver targeted advertising to web users in a way that is more privacy-sensitive and less reliant on third-party cookies. The goal of the Privacy Sandbox is to create a more privacy-sensitive ecosystem for online advertising that is better for both users and advertisers.
- Contextual advertising is a form of online advertising that targets ads to a user based on the content of the website or page that the user is currently viewing. This means that, instead of tracking a user’s behavior across multiple sites and using third-party cookies to build a profile of their interests and preferences, contextual advertising relies on the context of the user’s current browsing session to determine which ads to show.
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