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Human Intelligence: Shifting from ‘What?’ to ‘Why?’

Audience Engine's are the future of understanding the "why" behind customer engagements and make meaningful interactions

Human Intelligence: Shifting from ‘What?’ to ‘Why?’

Over the past few years, many marketers have begun to realise that they need to learn to earn; that they need to use customer insights to drive sales and revenue.  

The initial approach to this was simple. We’d reach out. We’d engage directly. We’d ask our customers what they needed, and question what they wanted. Effective? To an extent. Time consuming, labour intensive, costly, and subject to bias? Absolutely.  

It quickly became clear that new ways to learn about the customer journey were needed, sparking a widespread shift towards AI-powered customer data platforms.  

The problem? Most customer data platform (CDP) software tracks the ‘what?’; it tracks what is happening across the device. But as we all know, understanding customers - understanding humans - is very different to understanding computers.  

We don’t just need to know what’s happening. We need to know why it’s happening; we need that intent data to build more powerful customer experiences.  

AI gives us the ‘what?’. Human intelligence digs deeper to give us the ‘why?’. That’s why marketers need to shift from CDP tools to comprehensive audience engines.  

How CDP Software Works

CDP technologies are powerful for tracking online actions. They work by collecting and processing data about the devices that customers use, and the actions they take while using these devices. This generates insights which tells us that people using *this* device typically do this, and that people using *that* device typically do that. It helps us learn more about the relationship between device and action.  

This can be useful. But it also comes with some pretty notable limitations. The biggest issue, of course, is that devices are not specific to the individual. For example, a family of 4 may share one tablet between them, using the same sign-in details. In this instance, we have no way to segment data and assign it to just one individual.  

We know that *this* device is used in *this* way. We don’t know by whom. And perhaps even more importantly, we don’t know why. We don’t know the user's intent.  

As we all know, every journey - for every person - is different. Every experience is valuable. And so, we can’t afford to root our marketing strategies and internal processes in data that is not reflective of the actual behaviour of our customers.  

Introducing Human Intelligence Audience Engines  

Audience engines powered by human intelligence, rather than artificial intelligence, are different. Rooted in audience segmentation, an audience engine examines the real, authentic online behaviours of users themselves, on an individual level, regardless of what type of device they’re using, or the information they provide.  

When we shift the focus from ‘what?’ - what’s happening across the device - to ‘why?’ - the reason a person is taking that particular action - we’re able to use data analytics and real time analytics to develop and shape results-driven intent marketing strategies; strategies built around the unique needs of the customer.  

Armed with this sort of individual level insight, audience engines can make more accurate predictions about the next steps in the customer journey, enabling marketers to take the right action, at the right time, for improved conversions.  

Balancing Insight & Privacy

Of course, one major question to come from the ‘human’ aspect of ‘human intelligence’ is whether or not this sort of approach is ethical. Does it blur the boundaries between personalised customer experiences and basic privacy?

And the truth is that privacy *is* a big consideration when it comes to human intelligence. And the way to create a healthier balance between insight and privacy is to ensure you’re using an audience engine that values human rights.  

At Quin, that’s what makes us different. We understand the distinction between behavioural data and personal data, tracking elements such as purchasing patterns, viewed content, browsing patterns, and sequences of events, rather than personal demographics. Marketers need to know why people behave in the way they do; we don’t need to know who these people are to benefit from that insight.  

Building a Better Customer Journey

Shifting from ‘what?’ to ‘why?’ is valuable from a marketing perspective. But ultimately, it’s what’s best for customers, too. For example, when using artificial intelligence and CDP software, we might be able to see that a user abandons their journey at *this* point. That’s useful, as it tells us where things are going wrong.  

What it doesn’t do is tell us why things are going wrong at that moment. And that’s the sort of information we need to change, adapt, and build a better experience.  

When we’re able to answer that elusive question - ‘why?’ - we’re able to develop better experiences for customers. We’re able to ensure we’re always giving them what they want, need, and expect. At every stage of their journey.  

Gonca Gülser

Co-Founder