#user profiling

The Illusion of Complexity: Binary Exploitation in Engagement-Driven Algorithms

Abstract:

This paper examines how modern engagement algorithms employed by major tech platforms (e.g., Google, Meta, TikTok, and formerly Twitter/X) exploit predictable human cognitive patterns through simplified binary interactions. The prevailing perception that these systems rely on sophisticated personalization models is challenged; instead, it is proposed that such algorithms rely on statistical generalizations, perceptual manipulation, and engineered emotional reactions to maintain continuous user engagement. The illusion of depth is a byproduct of probabilistic brute force, not advanced understanding.

1. Introduction

Contemporary discourse often attributes high levels of sophistication and intelligence to the recommendation and engagement algorithms employed by dominant tech companies. Users report instances of eerie accuracy or emotionally resonant suggestions, fueling the belief that these systems understand them deeply. However, closer inspection reveals a more efficient and cynical design principle: engagement maximization through binary funneling.

2. Binary Funneling and Predictive Exploitation

At the core of these algorithms lies a reductive model: categorize user reactions as either positive (approval, enjoyment, validation) or negative (disgust, anger, outrage). This binary schema simplifies personalization into a feedback loop in which any user response serves to reinforce algorithmic certainty. There is no need for genuine nuance or contextual understanding; rather, content is optimized to provoke any reaction that sustains user attention.

Once a user engages with content —whether through liking, commenting, pausing, or rage-watching— the system deploys a cluster of categorically similar material. This recurrence fosters two dominant psychological outcomes:

If the user enjoys the content, they may perceive the algorithm as insightful or “smart,” attributing agency or personalization where none exists.

If the user dislikes the content, they may continue engaging in a doomscroll or outrage spiral, reinforcing the same cycle through negative affect.

In both scenarios, engagement is preserved; thus, profit is ensured.

3. The Illusion of Uniqueness

A critical mechanism in this system is the exploitation of the human tendency to overestimate personal uniqueness. Drawing on techniques long employed by illusionists, scammers, and cold readers, platforms capitalize on common patterns of thought and behavior that are statistically widespread but perceived as rare by individuals.

Examples include:

Posing prompts or content cues that seem personalized but are statistically predictable (e.g., "think of a number between 1 and 50 with two odd digits” → most select 37).

Triggering cognitive biases such as the availability heuristic and frequency illusion, which make repeated or familiar concepts appear newly significant.

This creates a reinforcing illusion: the user feels “understood” because the system has merely guessed correctly within a narrow set of likely options. The emotional resonance of the result further conceals the crude probabilistic engine behind it.

4. Emotional Engagement as Systemic Currency

The underlying goal is not understanding, but reaction. These systems optimize for time-on-platform, not user well-being or cognitive autonomy. Anger, sadness, tribal validation, fear, and parasocial attachment are all equally useful inputs. Through this lens, the algorithm is less an intelligent system and more an industrialized Skinner box: an operant conditioning engine powered by data extraction.

By removing the need for interpretive complexity and relying instead on scalable, binary psychological manipulation, companies minimize operational costs while maximizing monetizable engagement.

5. Black-Box Mythology and Cognitive Deference

Compounding this problem is the opacity of these systems. The “black-box” nature of proprietary algorithms fosters a mythos of sophistication. Users, unaware of the relatively simple statistical methods in use, ascribe higher-order reasoning or consciousness to systems that function through brute-force pattern amplification.

This deference becomes part of the trap: once convinced the algorithm “knows them,” users are less likely to question its manipulations and more likely to conform to its outputs, completing the feedback circuit.

6. Conclusion

The supposed sophistication of engagement algorithms is a carefully sustained illusion. By funneling user behavior into binary categories and exploiting universally predictable psychological responses, platforms maintain the appearance of intelligent personalization while operating through reductive, low-cost mechanisms. Human cognition —biased toward pattern recognition and overestimation of self-uniqueness— completes the illusion without external effort. The result is a scalable system of emotional manipulation that masquerades as individualized insight.

The Irish Council for Civil Liberties sent a report to the European Commission on Thursday (14 December), asking it to follow the Irish media regulator’s steps to turn off big tech’s algorithmic recommender systems. Recommender systems are the digital platforms’ inner workings that determine what content users see. In most cases, these systems use algorithms that tailor the user experience based on personal data. Major social media platforms use data such as the user’s search history or past purchases, but also on location, age, and the device used to build a profile often monetised by selling advertising space to third-party advertisers. Ireland’s new broadcasting and online regulator, Coimisiún na Meán, has published a draft binding code requiring video-sharing platforms like TikTok and YouTube to stop automatically using recommender systems based on extensive user profiling. Moreover, these companies would have to stop building profiles of those users whose age is unconfirmed or who are children. “Coimisiún na Meán is leading the world by forcing Big Tech to turn off its toxic algorithms. People – not Big Tech’s algorithms – should decide what they see and share online”, said Johnny Ryan, a Senior Fellow of the Irish Council for Civil Liberties (ICCL). -“The European Commission should learn from Coimisiún na Meán’s example and give everyone in Europe the freedom to decide,” he added.

Simply reaching your customers with marketing messages is no more an effective method. Honestly speaking, customers hate it when you bombard marketing messages in their mail and phone inbox.

Today, it is far better to engage customers via personalized messages. This is something that customers are looking for and the approach helps businesses to engage more customers with them. So, what are you doing to offer a personalized experience to your customers and engage them more?

The article will explain 4 easy and effective ways to boost customer engagement and personalized experience.

Greeting customers with personalized emails

Generally, marketers use customer’s first name in the subject lines. Well, the approach is not bad but you can make it better by adding more demographic information. Using demographics to your mail subject lines will definitely provide more personalized experience to your customers.

Create a proper customer profiling

If you want to improve sales by providing the best to your customers, profiling customers is good way to start. According to the need and demands of your customers, profile them based on their age, choices, previous shopping, gender etc. The method helps you to know each and every customer, what type of services they are expecting from you, what are their preferences etc. Customer profiling helps you to offer better-personalized customer experience.

Find out whether customers are active on social media or not

Knowing customers presence on social media is a big boon to you in your personalization strategies. By using modern technologies, figure out whether your customers are socially active or not. Try to find out answers to some questions, did they use any social media platform to login to your website, do they share your products or content with others?

If they did, it is a great news for you. You can drive lots of social information based on this, that will help in offering a personalized experience.

It's probably popped up on your television screen that the Senate and then the House of Representatives voted earlier this week, 215 to 205, to repeal an Internet privacy bill passed last year.  In case you're curious, here's a full list of every Senator and Representative that voted to repeal the bill and how much they received specifically from the telecom lobby right before voting. (local mirror)  By the way, if you would like to contact those Senators (local mirror) or Representatives (local mirror) here's how you can do so... When the bill hits Trump's desk it's a foregone conclusion that he's going to sign it.  Some of the talking heads are expressing concern about this, while others are cheering that the removal of this regulation is an all-around win for the market, blah blah blah... but what does this actually mean for you?

First of all, if you're reading this, welcome to the Internet.  You're soaking in it.

Second of all, please read this blog post (local mirror) by the EFF.  Just a few years ago, a couple of very large ISPs (that you're probably a customer of) got caught doing things like monitoring your web searches and hijacking them with different results they were paid to insert and analyzing your net.traffic to figure out what advertisements to inject in realtime.  The bill that just got repealed put a stop to all of that.

I've spoken to a couple of people who expressed disbelief that such a thing was possible.  In point of fact, intercepting and meddling with communications traffic goes back a very long way.  In 1994 a bill called the Communications Assistance for Law Enforcement Act (CALEA) was passed and codified as 47 USC 1001-1010.  In a nutshell, what this law means is that manufacturers of just about every kind of network-side communications device, from the telephony switches that route your phone calls to the carrier class routers that make up the network core have surveillance capability built in.  In theory, only law enforcement agents with warrants are supposed to be able to use them.  In practice, they're used all the time by employees of the companies that own that equipment to silently troubleshoot problems before they get too out of hand, and yes, they get abused all the time for petty shit.  As you may have guessed already, the moment that CALEA-compliant equipment was deployed back in the day hackers immediately figured out how to use them more effectively than even the telecom companies and silently eavesdropping on people using that functionality was a common "This is how 1337 I am" stunt.  So, please keep in mind that this "monitor all the customers" infrastructure is going to be badly abused and constitutes one hell of a security risk.

CALEA is regularly updated as communications technology evolves, and now encompasses things like the backbone of the Net, Voice-over-IP telephony, cellular telephony and companies whose business it is happens to be running wireless hotspots.  As it so happens, much of this functionality is perfect for monitoring customers' traffic, analyzing it, and packaging it for sale as large bundles of anonymized information or as discrete dossiers, ala Cambridge Analytica.  Let me paint you a picture, based in part of how things worked before that bill was passed originally...

Let's say that you're a customer of a large cable broadband ISP; let's leave the quality of your service aside because it's parenthetical to this story.  Let's also say that you're part of what passes for a nuclear family these days, three people, one of whom is nearing the age of majority in your country.  Let's call them Alpha, Beta, and Gamma; you can mentally assign identities and roles to them as you will.  Let us further assume that several companies that specialize in psychometrics for profit ala Cambridge Analytica exist in this story; they do in real life, so they do here, too.

Alpha uses their laptop for both work and personal stuff.  The ISP in use at home uses both traffic analysis and a super cookie silently inserted with a man-in-the-middle attack to identify Alpha's laptop; the machines that Beta and Gamma use have similar super cookies injected into their browsers.  The ISP also has a business unit, and happens to be the Internet connection provider where Alpha works, so they know that Alpha works for $bigcorp as well.  From watching the traffic at work they know that Alpha works for the business intelligence unit of $bigcorp because they constantly do web searches on other companies, read lots of papers and articles published by their competitors, and access a online news services that specialize in aggregating corporate news.  They can tell from Alpha's net.traffic that they seem to concentrate on just a half-dozen companies in the same business vertical.  This information gets packaged up and quietly sold through a subsidiary; two of $bigcorp's competitors regularly buy these bundles and with a little analysis figure out that $bigcorp is positioning itself to knock down both of their next quarter product releases, and start making plans.

At home, Alpha's starting to feel burned out from consuming dozens of papers and news articles a day, and aside from that $bigcorp is slipping a little in the market.  It's just not working.  Alpha has run a couple of web searches looking for web forums where they can talk about work life, work-life balance, and burnout.  After a few days, Alpha seems to have found two that seem homey and they've settled in on those forums, filled out a profile, and started posting there later in the evening outside of business hours.  Less obvious is that the health insurance company $bigcorp buys its insurance policies through is also a customer of the ISP's information retail subsidiary, and they mine that data to figure out what might be going on with its clients' employees.  Alpha sticks out immediately in the 25% percentile of risk for mental health - not serious enough to constitute a serious payout but over time they can bleed the bottom line (if you think insurance companies are in the business of health care, you're wrong - they're in the business of making money by investing the premiums paid by their customers, and paying for healthcare is the line they use to get people to pay them).  Somewhere in the insurance company's massive machine learning system (and what company over a certain size doesn't have one - this is the twenty-first century, after all) a daemon is set to watch Alpha's account activity.  If it trips a threshold, Alpha's premiums could be quietly increased to recoup the losses of the cost of psychological counseling.  Alpha's part of their insurance premiums are paid automatically out of their paycheque before taxes are applied, so unless Alpha is scrupulous about downloading and comparing each pay stub Alpha probably won't notice the raise unless something or someone brings it to their attention.

Beta's work life isn't anything to write home about for the purposes of this story, but they do have a hobby of note: Writing scenarios for roleplaying games.  Lately, Beta's been running Conspiracy-X and Eclipse Phase for the family tabletop group and has been doing background research for the scenarios they've been writing.  They've been running web searches on such diverse and interesting topics as blast yields for inefficient nuclear warheads (one scenario involves a terrorist group that's not sure it has the expertise to build a nuke, so their plan pivots to build a dirty bomb; thing is, how big would it need to be to put the whole city at risk?), covert radio transmission techniques (heroes' cells need to communicate somehow, and the characters need to be as devious as the person running the game), genetic engineering of bacteria (a bioweapon figures into the Eclipse Phase campaign), and solid rocket boosters (ditto Eclipse Phase - the heroes are supposed to find a cache of ancient rocket motors from before the fall of Earth; the question is, how would very old SRBs fail if somebody tried to fire them up?).  The ISP at home is, of course, monitoring all of this traffic; their machine learning system wasn't trained on anything approximating roleplaying games so all of the traffic to those games' official forums scoots under the radar.  A half-dozen law enforcement agencies have deals in place to get priority access to this information, and a few weeks after those games happen the entire family starts having major problems at every airport they fly to.  They even miss a few flights because they got red-flagged as being nebulous risks of the sort that the TSA loves.  So much for good, wholesome family fun.  On the up-side, some of Beta's web searches are quietly tweaked to return results that feature a couple of independent game publishers (a dozen of them got together at the hotel bar at the last GenCon and decided to pool their discretionary funds to buy their way up in the ranks at the ISP in question).  Now the family's discovered some interesting and offbeat games that they've added to game night.

Gamma's the odd one out that keeps throwing everything off.  Rather than buying everything off of Amazon like everybody else, they buy ebooks directly from the author; this includes a couple of textbooks a month.  They have their own gaming desktop but also use Beta's laptop from time to time to log into Fetlife, which associates at least two super cookies with that socnet profile (make that three - Gamma's hard drive crashed, which meant a full rebuild and a new traffic profile).  Beta has their own Fetlife profile, so that makes two profiles with three super cookies.  The traffic analysis system can't make heads or tails of it all, so occasionally both of them get advertisements for each other (or would, if Gamma didn't use an adblocker) and the occasional discount code for an off-the-wall store that sells veterinary equipment.  Gamma's also taking up knitting and whittling, two crafts that traditionally don't go together in the same person, so that further skews the traffic metrics.  As if that didn't mess with the surveillance profile enough, Gamma's going back to school soon and is doing a little prepwork, so brushing up on physics is resulting in search results skewed in the direction of a high school teacher working at a reasonably affluent private school; this means lots of advertisements for laboratory equipment in bulk, lesson plans, and stupidly expensive peripherals for computers for physics experiments.  The metrics never really settle down, so the sales projections for the entire family get thrown off.  Even though there are at least three people living at that subscription-household, the activity and metrics are all over the place.  Is all this sales activity related?  Is it not?  Do they have a communal computer in the house that visitors are allowed to use (the telemetry from the router suggests this is the case)?  Should they be charged more for this?  The ISP can't tell.  This also has the net effect of messing with everybody who buys their aggregate traffic profile so once in a while they get weird advertisements and discount codes for the most random stuff, like veterinary equipment and truck tires.  When deep learning systems screw up, they screw up.

In this competitive world just knowing customers is not enough. You should have the best understanding of your customers, deep customer insights.

As a marketer, you should know what are their greatest fear, desires, and motivations. Try to understand what they want, figure out why they choose you over competitors and what are the reasons that make them happy.

There is an old saying, “Understand your customers and sell what they want”. Well, that's true, rather than forcing customers to buy your products offer them what they really need. This approach will help you to boost sale.

For knowing them, profiling your customers is an important process. Customer profiling is one of the best ways to point out the group of customers who are most likely to buy your products and needs your services.

Here are some reasons why profiling customers is important.

Helps in identifying niches with low competition

Now, you have profiled collection of data. Out of this, you have to choose the group of customers where the competition is low. Once you figure out the particular group, you need to focus more on that group and offer services accordingly.

Increases profits

Once you know which is your targeted group of customers, you can offer more customized offers to them. Customized services will likely help in developing customers interests and lead to purchase products. That means more profits.

Helps in exploring new opportunities

One of the benefits of profiling customers is that it helps marketers to explore or find new opportunities. They can use resources and strategies based on their profiles customers data.

Attracts more potential customers

Retaining existing and gaining new customers are the two important factors for the success of a business. Along with old customers, it is must for businesses to catch new customers. Profiling helps marketers to attract more customers related to their niche.

Improves communication

Communication is one of the most important pillars to retain existing customers. If you know who are your customers, their identity, needs, demands, it is easy for you to communicate with them. Communication helps businesses to build a healthy relationship with their customers.

Before we start looking at what it costs to achieve proper profiling of users, we need to have a look on why an organization needs it.

Marketers are frequently approaching both their existing or potential customers to gather data as much they can. They know how important is data for them. But do you think only collecting data is enough? Well, the answer is no.

Data is generally a collection of collective information. You can't predict who are your real customers with this collective data. It is a mixture of different types of customers regardless of age, sex, interests etc. All you need to do is profile them and the process of profiling users from the collective data is called user profiling.

User profiling helps you know the customers in a very different manner. Before profiling, only you have a heap of customers data (useless until profile it wisely). But once when you profile them based on a different factor, this data will do magics for you. Let’s see what are benefits of profiling users.

Get in-depth knowledge about customers

You can classify your customers based on different factors such as age, needs, demands, previous shopping experience etc. Now you have fragmented data. It helps you to know each and everything about customers.

Helps in creating more effective policies

One you know the needs and demands of your customer, you can create policies accordingly. You can also provide offers that benefit users once you have an idea of what they want.

Helps in providing personalized experience

When I get emails in which my name is mentioned, I like them and definitely open and read them. I also like it when my mobile data plans are planned according to my need. Who doesn’t like personalization? Everyone likes it.

With profile data, businesses can offer personalized services to their customers. Personalization is one of the most important pillars of business success.

Improves users experience

User experience- the factor which decides the future of a business. To beat your competitors, you have to offer services in such a way that users will like you. The services you offer will boost their experience. Don’t forget to stay ahead, you need to provide best user experience.

Your marketing team is trying their best to drive traffic to your website and they are getting success in driving varieties of visitors. But having too many visitors is not going to help you out until you profile them according to the different factors. In this article, I am going to show some ways that help you to profile your customers detail.

Decides who your potential customers are?

You will get lots of visitors, but not all are your customers. You need to find out who are your potential clients. Divide your visitors into different groups based on their experience and needs. Divide them according to their age, sex, demands, previous experience, etc. Profile your users based on the mentioned factors.

Ask questions

Come up with a set of questions for each group that you classified. Some of the questions are:

What do they like most about your product?

How can you do better (ask for suggestions)?

What are they expecting from you?

What are their needs and demands?

Is the cost of the product is right or not?

Asking such questions to your customers helps you to create better marketing policies. Such questions help businesses to know what their customers are thinking about them.

Concentrate on high priority customers

Focus on your essential customers first because they are the one who drives profits for you. Put your all efforts to make them happy and fulfill their demands and needs. But that doesn’t mean to ignore the rest, listen to them too and make policies accordingly. Try to build trust among your customers.

Always stay in touch with your customers

Everyone likes personalization. Be in touch with your customers on the regular basis. The process helps you in knowing your customers more. The process gives a touch of personalization and personalization builds trust.