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Cookiepocalypse - reinvent your first-party data strategy with data streaming

Massimilano Ciccone Profile picture for user Massimilano Ciccone March 15, 2023
Summary:
That's the way the third-party cookie crumbles - Massimiliano Ciccone of Confluent addresses the challenges and possibilities of tracking and benefiting from the right data as the digital advertising industry undergoes a radical change in data privacy.

chocolate chunk cookies on a bright blue background © abrikaSimf - Shutterstock
(© abrikaSimf - Shutterstock)

The death of the third-party cookie is approaching. Firefox and Safari phased out 3rd party cookies in 2013 and Google has announced it will do the same for Chrome – which accounts for 56% of the web browser market – in 2024.

While Chrome’s third-party cookie ban signals the final death knell for cookie-based tracking, the end had been a long time coming. Once public awareness of data privacy began to grow, regulation followed, forcing the digital advertising industry to completely rethink its approach to data.

At the same time, we are also witnessing a steep increase in customers expecting consistent, personalized experiences across the myriad of channels they interact with — whether in-store, on the website, or in an app.

According to a recent GetApp survey, 41% of marketers believe their biggest challenge will be their inability to track the right data. So how will marketers and advertisers gather, share, and benefit from user data in this new world order?

Tracking alternatives and the rise of first-party data 

There are a number of alternatives already on the horizon. Tech giants have begun to make moves to mimic the old user tracking behavior but at the same time protect privacy as a commercial differentiator.

Browser-based tracking 

Ad technology currently in testing stage includes Google’s browser-based tracking (i.e. Privacy Sandbox), which anonymously assigns the data for a website visitor to a cohort of several thousand people with similar interests and saves it locally on their computers, in order to provide interest-based advertising in a privacy-preserving manner.

Advertisers would then be able to target them based on the cohorts they belong to, rather than on an individual level. Google says: 

Our web products will be powered by privacy-preserving APIs which prevent individual tracking while still delivering results for advertisers and publishers. 

Although Google claims the APIs will be made available to other browsers like Safari and Firefox (so that advertisers could end up with a more stable view of users across the ecosystem), the solution is seen by some as a move to consolidate monopoly and revenues. Tanya Field, chief product officer for advertising tech vendor Smartpipe, said: 

With this move, Google could build [its] own replacement and take ownership of both the solution and the budgets. 

Identity-based tracking

Other identity-based tracking solutions leverage the idea that a central authority would assign every web user a unique advertising ID based on a data point that isn’t likely to change very often, like their email address or mobile number. This is in contrast with the device-based targeting of cookies, where a device could have multiple users.

Adtech companies would then once again be able to monitor individual users’ browsing habits, serve them targeted ads (in a consensual and privacy-preserving manner), and measure their effectiveness.

Assuming a wide adoption of this model, the digital ad world could still rely on a user-tracking system that would look very similar to the cookie-based system we have today but would result in a more consistent and relevant experience for the consumer, which is important since 70% of people are comfortable receiving advertising online as long as it’s relevant to them [sociomantic.com].

First-party data tracking 

Alternatively, first-party data tracking is where publishers collect data about their audience’s content preferences and share that with brands who are collecting their own data about customer shopping habits.

If there are matching data points across both datasets that can identify a particular user, publishers and brands can team up to provide a more powerful and targeted ad.

This approach could prove difficult to implement since it would require direct deals between the parties and the setup of complex technical systems that would enhance ad personalization while protecting the respective customer data.

The drawbacks of tracking alternatives

With all these alternatives, the problem remains that there is still room for inaccuracy.

Browser-based tracking doesn’t overcome the fact that a family of four might use the same browser, meaning a mother and son will get served the same ad even though they have very different interests. And no matter how high-level, consumers will probably feel reluctant to give data over to publishers when they know it’ll be shared with a retailer or other brand.

Users' data has to be worked with in a way that protects privacy and anonymity while offering accurate, contextualized, and actionable insights that match consumers with what they actually want – not what third parties speculate that they might want.

One solution that does not require agreement on new standards and complex data sharing is the harvesting offirst-party data alongside consensual zero-party data. Broadly, zero-party data is a type of data that you collect directly from your audience. Think actions taken on an app, insights from social media, direct feedback from surveys, or data in your CRM software.

Companies could then use this combination of data to show customers relevant, targeted ads (as opposed to relying on third-party data flogged by ad-tech companies) and directly measure their effectiveness.

Know your user with first-party data in motion

We are beginning to see organizations acknowledging the opportunity of elevating in-house first-party and consensual zero-party data to reinvent their business model and change the way they understand consumers' needs to drive a personalized omnichannel experience. To do this, companies need to invest in its collection and analysis in a way that is meaningful.

By meaningful, I mean collection and analysis that has the ability to observe a user’s interaction with a business (called “events” in the streaming data world) in a 360-manner.

The era of one-too-many marketing engagements resulting from siloed teams working on siloed tools is over. To scale engagement across all of your tools and channels, you need a unified view of customer data at an individual level to deliver timely, relevant experiences with consent.

Ultimately, zero and first-party data offers an opportunity to really know your own user, opening up new avenues of powerful personalization. Since this data is generated on your estate, it makes it easier to control, collect from the moment it is produced, and feed it to the systems that deliver the customer experience in real time.

Building a successful first-party data strategy 

But although easier to obtain, accessing real-time, high-quality first-party data from across an organization remains a challenge. Building a first-party data strategy requires a robust yet scalable customer data supply chain to move from collection to unification to activation.

But the right strategy is nothing without the right technology.

Centralized data lake and warehouse architectures, while successful, are not perfect. They often result in inaccessible data swamps and governance where data scientists don’t have a full understanding of the context.

Businesses are struggling with slow batch systems and the individual, custom integrations that need to happen to connect every app, every database, and every SaaS product on a one-to-one basis. It’s heavy, it’s slow, and it breaks down with even small changes to data structures.

Look to construct a decentralized architecture

Decentralized architectures have become much more popular recently, along with the rise of a data mesh model.

Data Mesh is a rethinking and reformulation of many of the best principles of data architectures, emphasizing data as a first-class citizen and not as an afterthought. Building and operating a modern data architecture at scale requires addressing the needs, roles, and responsibilities of everyone involved with the data.

Data products based on event streams help fulfill these needs, such as real-time processing, unification of operational and analytical sources, and integration with existing batch-based systems. Together, an event-streaming data mesh forms a new socio-technological compact that makes it easy to access and use trustworthy data to power your organization.

The critical applications in a modern software-defined business are about delivering end-to-end digital customer experiences, and fully integrated real-time operations. These systems must cut across infrastructure silos and continually react, respond, and adapt to an ever-evolving business in real time.

To accomplish this, we need an infrastructure that supports collecting a continuous flow of data from across the company and is able to persist, process and immediately react to that flow of data in real time. In other words, as a company increasingly becomes software-defined, it needs a data platform built for data streaming, what we like to call data in motion.

Real-time data streaming is the next step for customer tracking

Real-time, contextualized data streaming can provide more accurate marketing data.

Ads, discounts, or incentives are no longer offered based on guesswork about demographic interests or historic behavior across the web, but on actual behavior in the moment. You could, for example, see a user who has been eyeing up a pair of sneakers online and offer them a discount to nudge them into the checkout process. It’s the digital version of bartering at a market stall.

Data in motion can help organizations create connected customer experiences by stitching together ‘facts’ into an immutable single source of truth that is updated continuously. At the same time, it can be asynchronously distributed to applications and systems across the organization, integrating with other products and informing insights at various levels, at the right time.

With this kind of technology, companies can upsell and cross-sell to customers within their walled gardens; constantly improving their experience and providing a better service.

Customers grow more engaged, more loyal, and keener to willingly share personal data as a consequence. There is no longer a reliance on third-party ad revenue.

I know this sounds like an ad-free internet utopia. But the death of third-party cookies is in my view a sign of a real revolution in the way we think about advertising. Data in motion will be what takes us there.

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