Rafeeqah Mollagee, Head of Data & Analytics at Incubeta, explores how AI and first party data can help marketers in the cookieless world.
Everyone knows that third-party cookies are on their way out, even if they are pretending that they are not. And yes, we might sound like a broken record but we need to keep talking about it. Companies are about to lose a vital part of their legacy marketing strategy, so this is BIG.
And, while you may have heard it before, time has now run out. Which means you need to act. Right now. I mean, hopefully you already have, but if not, it is time to start developing new data strategies and incorporating future-ready tools – such as Google Analytics 4 – into your plans.
Two reasons for this. Firstly, so that you can compare your first-party datasets with the ones you are currently collecting to see how everything weighs up, what you can still use, and what will need to be replaced. And secondly, so that you can start to build an alternative way of measuring performance.
This second point is crucial because, let’s face it, measuring performance and providing accurate KPIs for marketing campaigns has always been a pain. Even with third-party cookies. Now, as they are being taken away, we need to find a new way of proving the success of our immensely creative work and showing its return on investment – or see our marketing budgets get slowly stripped away.
So yes, it is another article about the depreciation of third-party cookies. But it’s also an article that just might help your marketing funds survive in the post-cookie world of the future.
Time for a change and first-party data is the way to go
Investing in a first-party data strategy is no longer an option for marketers, it’s an out-and-out necessity. It is the most obvious replacement to third-party data, providing an unparalleled level of insight into a customer’s journey and their interactions with your brand, not to mention it’ll play a big part in filling the third-party cookie shaped hole. All it needs to work is for the consumer to be offered something of value in exchange, such as exclusive content, personalized recommendations, or specially curated offers.
Not only does this heighten the customer experience, rebuilding trust and loyalty in the brand-consumer relationship, but it can also improve the accuracy of measurements. This is because the insight gained will be unique to your business and customer interactions, coming straight from the customer themselves.
That being said, it’s important to bear in mind that, to gain accurate measurement from first-party data, there must be a robust tagging infrastructure in place. This needs to be designed for first-party cookies, consider all platforms in the marketing mix, and make use of good data governance practices. It must be adaptable enough to evolve alongside future industry changes, considering privacy solutions like Google’s Enhanced Conversions and Consent Management.
Though creating an effective first-party marketing strategy and implementing a new tagging system may take some effort to begin with, the benefits far outweigh the negatives – offering those willing to put in the groundwork with a holistic view of the customer journey and more accurate data that can be used to measure and optimise their campaigns.
The AI race is on
First-party data will be able to replace a lot of what is lost when third-party cookies are given the boot, but it is inevitable that some observable data will be lost. Fortunately, however, technology has provided us with an efficient way of filling any gaps – using the formidable power offered by AI and machine learning (ML).
These technologies are capable of processing and analyzing huge amounts of data at once, to find patterns and trends that might not be easily detected by humans. In doing so, they can help marketers pull actionable insights from their newly filled pool of first-party data and help drive their campaign strategies.
The potential of AI and ML is vast, opening the door to various improvements in predictive analysis, customer segmentation and personalization, but a particularly notable application for measurement is conversion modeling.
In a nutshell, conversion modeling uses AI and ML to analyze customer behavior and identify the patterns that lead to conversions. It can then extrapolate these patterns to predict the likelihood of conversion from a certain type of customer behavior or interaction.
The real beauty of this application though, is that it does this while maintaining privacy. It uses aggregated and anonymized data, thereby avoiding the need to track individual user behavior, and bridges the gaps left by the deprecation of cookies. This allows for a more accurate allocation of conversions to traffic sources, improving attribution measurement and making it easier to allocate budgets when planning campaigns – presenting an ideal solution for the privacy-centric, cookieless future that marketing is heading towards.
You never know unless you try
At the end of the day, as we move towards a new era of marketing, there will be a need for experimentation. Google may have delayed the deprecation of third-party cookies, but that doesn’t mean it’s time to sit back and wait. Instead, roll up those sleeves and start playing with first-party measurements, new tagging infrastructures, AI and ML technology and conversion modeling.
Similarly, to any new technology or business strategy, trial and error is a vital part of getting it right. Not everything works the first time. Marketers need to take the time to trial new tools and strategies with first-party data to find out what works best for them, identifying strengths and weaknesses. Ultimately, through trial-and-error companies can understand how to cope and thrive in a cookieless world.
But by opting for a trial and error approach, marketers are putting themselves in the best position for when Google does deprecate cookies in 2024. There will be no need to mitigate any disruption since you will be prepared to continue your marketing campaigns without worrying about losing your cookies.
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