Why Good Marketing Analytics Still Starts With Human Behaviour

Marketing has never had more data.

We can see what people click, where they drop off, what they search for, which products they compare, how long they stay on a page and whether they eventually buy.

And yet, having more data doesn't necessarily mean understanding consumers better.

That's something I've increasingly realised while studying Marketing Analytics. The technical side of analytics is incredibly useful, but the numbers become much more interesting when you start asking what human behaviour sits behind them.

A metric is an outcome, not an explanation

Imagine an e-commerce dashboard showing that a product page receives plenty of traffic but has a relatively low conversion rate.

The numbers tell us what happened.

They don't immediately tell us why.

Maybe the price feels too high. Maybe consumers don't understand the product benefits. Perhaps the images aren't answering the questions they have. They could be comparing alternatives elsewhere. Or maybe they simply weren't ready to purchase.

The same behavioural outcome can have completely different explanations.

That's why I think one of the most important questions in analytics is surprisingly simple:

“What might the consumer be experiencing here?”

It shifts the conversation from reporting a metric to investigating a behaviour.

The class that changed how I looked at consumer behaviour

During my Marketing Analytics specialisation at EDHEC, one subject I particularly enjoyed was Advanced Consumer Psychology.

We explored motivation, perception, emotions, decision-making and some of the less obvious ways context can influence behaviour. The course also used experimental research to explore how psychological theories can help us interpret consumer decisions.

One experiment from class that stuck with me involved something I definitely wasn't expecting to discuss in a marketing lecture:

a curried grasshopper.

Participants were placed in different conditions. Some mentally simulated pulling the product towards themselves, while others imagined pushing it away. There were also control and other experimental conditions. They then evaluated the product on measures such as desirability, taste and willingness to pay.

What fascinated me was that the approach condition produced a more positive evaluation. The explanation discussed in class was that we generally approach rewards, so the sensation of approaching something can itself influence how positively we feel about it, even when the object isn't particularly appealing in the first place.

It sounds like a small experiment, but it made something click for me.

“We can measure that an evaluation changed. Psychology helps us ask why it changed.”

And that is increasingly how I think about marketing analytics.

The interesting part sits between psychology and data

Analytics helps us identify patterns.

Consumer psychology helps us think about what might be creating them.

Neither replaces the other.

Our Ubisoft × EDHEC project was a good example of this intersection. The challenge involved understanding why players remain engaged with games or eventually leave them.

At first glance, retention sounds like a straightforward analytics problem: measure activity, identify churn and analyse behavioural patterns.

But once you start looking at the people behind those numbers, the problem becomes more nuanced.

Players might stay because they enjoy improving their skills. Others value playing with friends. Some care about fresh content, competition or simply having fun.

Suddenly, retention isn't just a number.

It's a behavioural outcome influenced by different motivations.

What people say and what people do

This is also why I find combining different forms of research valuable.

Behavioural data can show us what people do.

Surveys and interviews can help us understand what people say they think or feel.

Community conversations can reveal what people discuss when nobody is directly asking them a research question.

None of these sources is perfect on its own.

But together, they can provide a much richer picture of the consumer.

That's also one reason I'm interested in developing my analytical skills further. I don't see tools such as Python, statistical analysis or dashboards as the end goal.

They're ways of asking better questions of data.

From dashboards to decisions

Ultimately, marketers don't need insights simply because they're interesting.

They need them to make decisions.

Should we change the way a product is communicated?

Is there friction somewhere in the customer journey?

Which consumer segment should we focus on?

Why are people disengaging?

What should we test next?

For me, that's where marketing analytics becomes most valuable: when a pattern in the data leads to a better question about people, and that understanding eventually leads to action.

People first. Data to understand them better.

The more I learn about analytics, the less I see marketing as a choice between creativity and numbers.

Good marketing needs both.

Consumer behaviour gives the numbers context. Data helps challenge our assumptions about consumer behaviour. And marketing sits somewhere between the two.

Perhaps that's why the idea behind my portfolio ended up being:

“Curiosity about people. Decisions backed by data.”

It captures the kind of marketer I'm trying to become.