Personalization based on other data

Most personalized ads we see are based on the simplest of data; name, location, purchase history, browsing history and click history. This level of basic personalization usually leads to some pretty simple results; in fact, you only need to have experience with retargeting to realize that an ad featuring that washing machine you left in a shopping cart is now stalking you around the Internet.

This initial and also blunt approach was heralded as the beginning of personalization, but has not gone much further than this. There is a need for more data to be shared to move to truly personalized and relevant ads, and that is where we run into challenges.

The DDMA report tells us that people are concerned about their personal privacy. “The corona crisis has caused half of people (48%) to think more about sharing data. In doing so, 57% of the Dutch are concerned about privacy - this percentage is as high as two years ago. Less than half (42%) feel that privacy law AVG provides better data protection. In addition, not everyone is aware of their rights under this law: 60% know that they can ask any organization to see their personal data.” As a result, there is a growing conflict between consumer privacy and personalized advertising from organizations.

Internet users are aware that they are leaving an ever-growing trail of personal data wherever they go. An awareness that is increasingly evident with the European introduction of GDPR.

Privacy concerns stem partly from a better understanding of what personal data is worth and partly from a lack of trust. High-profile data breaches like e.g. with the GDPR, the “stalker” nature of some personal ads and the severity of the AVG are causing consumers to be more aware but not always feel in control.

This growing concern about our privacy and personal data does not end there. The tech giants are responding by giving customers more control in the first place; cookie permission, iOS14 asking for tracking permission and Facebook making it easier for consumers to see “their interests” are some of the more obvious ways the tech giants are increasingly respecting privacy.

Google is going a step further, blocking all third-party cookies in Chrome starting in 2022. All of these factors combine to signal the end of tracking personalization as we currently know it.

These measures thus push the need for organizations to build trust with consumers and give them back control. This, of course, directly impacts the most common types of personalized ads we see in today's digital landscape. That's why it's important to make your customer funnel insightful for your organization; we do that using the See, Think, Do & Care model.

Simply put; we need to think differently. We need to use different and diverse data sources to make decisions and think about the relationship brands have with consumers and what data is needed to do so.

Consumers are more aware than ever of the commercial value of their data. Also, about why nurturing this set of data more transparently and openly can lead to better customer-centric service as well as much better personalization for them. They know it! The only reason consumers say data sharing is attractive is if it benefits them personally. They want to see relevant and representative ads, not be chased.

Then looking at data in a different way, social platforms have been talking about signal-based intent for a while now. Brands are starting to evolve from user demographics to individually personalized experiences. These brands are enabling artificial intelligence to understand behavior and tailor experiences based on intent.

What is clear is that the experience must take into account the entire customer journey. Still, with the changes in cookies and tracking on the horizon, this will be a challenge for anyone interested in winning the race.

As many brands strive for mass personalization, there is a need to respond to behavior and match messages to be delivered at an optimal time, creating highly personalized digital experiences for specific audiences based on a set of criteria. This goes beyond the kind of tracking and demographic data we have traditionally used for personalization. The criteria are formed by using multiple sources of behavioral data plus a layer of logic (most likely AI-driven) to understand what that customer is looking for, why, when and how. So we can deliver more meaningful content, in more ways and through more channels.