Emptying The Cookie Jar With The Help Of AI

It looks like the cookie’s days are numbered as a useful tool for marketers.

As Google’s Chromium team announced that it was upping the privacy requirements around cookies at the Google I/O conference, users have been empowered to be even more selective about what data is -- or is not -- shared from their browsing behaviour.

RIP, cookie, we hardly knew ye. But of course, cookies knew us very well.

Third-party cookies have played a fundamental role in allowing marketers to deliver targeted digital advertising. There is a sense that without cookies, marketers lose vital insight into the customer journey.

But in truth, the Multi-Touch Attribution (MTA) model is less and less fit for purpose. There are more media channels, new publishers and new ways to buy media -- and a cookie-based system just can’t account for all of it.

Marketers should not be lamenting the death of the cookie, because there is still more opportunity in terms of customer data and insight out there. 

Look at the amount of customer interaction that happens offline (yes, there is a still a world beyond the internet). Cookies can’t track that. Rely solely on MTA and the customer literally "goes dark." But that isn’t the end of the data they are generating.

In letting go of the MTA comfort blanket, marketers have the opportunity to explore alternative techniques that create valuable insights.

These might include the value of brand equity or the impact of individual circumstance, which in turn leads to the potential for a much finer personalisation approach.

Brands don’t lack for data, but they need to look into the sort of data that is going to give them the best results. This will lead them naturally to AI-led solutions.

Modern AI tools are capable of navigating and processing huge pools of complex and incomplete data, testing and learning autonomously at a speed no human could reproduce.

This will be vital as volumes of structured and unstructured data collide and marketers look to integrate insights such as context to develop hyper-personalisation.

AI and data-focused marketers are a match made in heaven -- huge volumes of data need AI to make sense of the noise, while AI systems rely on huge volumes of data to work properly.

Naturally, the mention of AI strikes fear into the hearts of many marketing departments. For the particularly excitable, it conjures images of robots gone rogue.

More pragmatic marketers view it with a mixture of trepidation and suspicion -- trepidation because many are just coming to grips with the concept of Martech as a whole, and suspicion because it remains little understood by the sector, and occasionally even by those who claim to deliver it.

Certainly, there are large-scale providers such as IBM Watson doing some cutting-edge stuff and leading the market, and global organisations -- particularly the digitally native such as Lyft, Airbnb and LinkedIn -- are building AI in-house, since they have access to skilled staff.

But AI-powered martech is accessible to all, and all should be accessing it. It isn’t a question of handing everything over to the machines and seeing what they spit out.

Successful marketing-powered-by-AI means humans and machines working together, taking the insights revealed by the advanced analytics and applying it to experience and creativity.

To future-proof our businesses, marketers need to move beyond basic data analytics and embrace systems that can deal with the constant increase in complexity that we face.

Combining our human expertise with AI-translated data is the best way to make sure we don’t lose out further to big tech players, or forever surrender ourselves to walled gardens.





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