Cookies, Vikings, and Algorithms (And why the future belongs to those who understand human behaviour).

I have spent the best part of the last seven years working on things that, on the surface, have absolutely nothing to do with each other:

• Building a retention system for an international gym chain, because I genuinely could not accept the idea that the entire industry should operate like a revolving door, one person in, one person out.

• Proposing the transformation of a basic sales tracker into a behavioural intelligence system, combining WiFi, point-of-sale, and demographic data to understand why people behave the way they do inside a physical space, presented to the leaders of a top five UK university.

• Forecasting Australian cookie sales by flavour using regression models.

• Rebranding a marine technology company using Norse mythology.

• Building a machine learning model using 36,000 YouTube videos to predict what makes a video trend.

• Doing a public talk discussing the future of social media as a predictive science.

  
  

If you read that list without any context, it looks like someone who cannot pick a lane.

And I understand why.

But to me there was always a method to my madness, because not once did I see them as different.

Every single one of those projects was about the same thing.

Understanding human behaviour well enough to measure it, predict it, and design for it.

And I believe that ability is about to become the most valuable skill in the world.

The Tipping Point.

We are producing more data about human behaviour than at any point in history.

We have more ways to measure individuals and populations than ever before.

And the tools to analyse that data at scale are becoming more powerful and more accessible every single year.

What that means, and I do not think most people have fully understood this yet, is that we are approaching a tipping point.

A point where understanding and influencing human behaviour at scale is no longer reserved for the biggest technology companies on the planet.

It is becoming available to anyone willing to learn how it works.

The companies that already understand this, Meta, Amazon, Apple, and Google, have been doing it for years.

They have built entire empires on the ability to measure what people do, understand why they do it, and design systems, products, and environments that influence what they do next.

Shoshana Zuboff wrote an entire book about it.

The Age of Surveillance Capitalism is, in many ways, a detailed account of how these companies turned behavioural surplus into one of the most profitable business models ever created.

But here is what most people miss.

The reason that knowledge has not spread more widely is not that it is secret.

It is that these companies have the most advanced datasets, alongside the behavioural scientists, data engineers, psychologists, and systems designers to match.

Together, these allow them to build systems that keep their growth moving faster than the rest of the world.

And they have had very little incentive to share what they know.

But all of this is starting to change.

New research is emerging.

Books, case studies, and practical and technical methodologies are becoming available to anyone willing to read across disciplines and connect what they find.

And the people who do that first, who learn to work across behavioural science, data, creativity, and psychology rather than inside just one of them, will be the ones who shape what comes next.

For better or for worse.

What It Looks Like in Practice.

I have seen what happens when you apply this thinking, even at a small scale, and the results are just fascinating.

Redesigning the Customer Journey.

At a gym, I did not just design a sales and marketing strategy.

I redesigned and improved the entire customer journey.

I analysed member behaviour across thousands of people across multiple locations.

I mapped when and why they were leaving, and introduced a series of strategic nudges:

• Changes to the cancellation process.

• New membership structures.

• Personalised touchpoints at critical moments.

These together fundamentally changed the economics of multiple sites.

Churn dropped.

Revenue that would have walked out the door was retained.

And the system kept compounding because every intervention was measured, tested, and refined based on what the data actually showed.

That wasn’t marketing, it was a form of service design combined with choice architecture, directly applied to a commercial system.

Measuring Human Behaviour in Physical Space.

At a university, I proposed something similar but in a physical environment.

I looked at an internal sales tracker that the data team had made.

Everyone else couldn’t see it as anything more than face value.

I saw the opportunity for us to understand behaviour at a much deeper level.

By integrating WiFi data and door counters (for data quality), with point-of-sale transactions and demographic information (provided via the campus WiFi), you would be able to see not just what people were buying.

You would be able to see who was consuming what, when, and where.

You could then test new initiatives and measure whether they actually changed behaviour.

You could optimise the physical environment, the provision of products and services, and the way commercial spaces were designed.

All based on real evidence of how people actually move through and interact with a space.

That was not data analysis, that was behavioural science, choice architecture, and consumer psychology applied to physical space design.

Labelling What Nobody Else Was Measuring.

At an agency, I wasn’t happy with the standard delivery of basic monthly reports.

So I decided to download raw platform data exports and label the underlying variables that influence engagement behaviour with a brand.

Not just what content was posted, but the specific combination of format, theme, timing, creative composition, and copy structure that determines whether a human being actually stops scrolling and pays attention.

When you measure at that level, you stop guessing and start seeing patterns that most marketing and social media teams never know existed.

Across multiple accounts and sustained periods, engagement lifts of 60, 70, 80 percent and follower growth up to 300% became possible.

Not through posting more and chasing trends, but through understanding the mechanics of attention and designing for them deliberately.

That was not social media management, it was behavioural data science applied to content strategy.

The Direction of Travel.

The thread connecting all of these projects is the same principle operating at different scales.

In complex environments, the more you can define, measure, and learn from, the better your decisions become.

Human behaviour will never be as clean as a chess board because people are messy, context shifts, motivations change for reasons that no model can fully capture.

But the direction of travel is the same.

The more variables you can identify, the more interactions you can measure, and the more noise you can remove, the smaller the margin for error becomes.

And the closer you get to placing each domino deliberately so that it increases the probability of the next one succeeding.

Most businesses operate in silos.

Marketing does not talk to operations.

Data teams do not talk to creatives.

So the real opportunities to make a difference are missed.

Because the ideas that will create disproportionate results exist at the intersections between all these disciplines.

Those who see the intersections first will be the ones who massively outperform everyone else.

The Apples, Googles, Amazons, and Metas of this world prove this is no longer theoretical.

It is already happening.

And it has been matured into a science for years.

I have seen it work in gyms, in universities, in agencies, across construction, maritime, sport, energy, and technology.

I have stood on stages and presented this thinking publicly, connecting machine learning, behavioural science, and content strategy in front of rooms full of people who had never seen those disciplines discussed as parts of the same system.

And my academic research, from an undergraduate dissertation on human attention, through a master’s in business analytics and machine learning, to a current dissertation exploring whether organic social media can be treated as a genuinely predictive behavioural science, has been one long attempt to understand and formalise the principles behind it.

All this has led me to believe that…

The Future Belongs to Those Who Understand People.

The future will be defined by those who can measure, understand, and design systems that influence human behaviour.

And who have the tools and systems to do it at scale.

The knowledge to do this is no longer locked behind closed doors.

It is available to anyone willing to sit across disciplines, connect what they find, and apply it with creativity and test with a sense of wonder.

But the question is not whether this shift is coming.

It is already here.

The question is whether you will ride the wave or be overcome by it.

- Mike