What an algorithm is and why it matters in marketing

The word "algorithm" has become one of the most used and least understood in digital marketing. Every time a team laments "the algorithm changed," every time money goes into SEO to "please Google's algorithm," every time a creator analyzes why their TikTok video didn't get reach—they're all referring to a technical concept with a twelve-century history and implications that, in 2026, go far beyond "what do I do to get my post more likes."

This article walks through what an algorithm actually is, where the word comes from, how the algorithms that most affect today's marketing work, what changed with the arrival of generative AI models, what recent European regulation says, and how to work with them without falling into superstitions or pointless attempts to "trick them."

What exactly an algorithm is

A rigorous definition: an algorithm is a finite sequence of well-defined instructions for solving a problem or carrying out a task.

The definition has three important elements:

• Finite: the algorithm ends in a limited number of steps.
• Well-defined: each step is clear and unambiguous.
• Solves something: it has a purpose.

A cooking recipe is an algorithm. The assembly instructions for a piece of furniture are an algorithm. The long division we learned in elementary school is an algorithm. The difference between these everyday examples and software algorithms is just the speed and volume of data: a computer can execute billions of steps per second over datasets no human could process by hand.

The origin: a ninth-century Persian mathematician

The etymology is beautiful and little known.

Muhammad ibn Musa Al-Khwarizmi (c. 780-850) was a Persian mathematician, astronomer, and geographer who worked at the House of Wisdom in Baghdad—the most important academic center in the Islamic world during the Golden Age. His treatises on arithmetic and, especially, his book "Al-Kitab al-mukhtasar fi hisab al-jabr wal-muqabala" (around the year 820), introduced the Arab world to the Hindu-Arabic numerals we use universally today and formalized systematic procedures for solving equations.

When his works were translated into Latin in the Middle Ages (12th century), his name was Latinized as Algorismi or Algoritmi. The word "algorithm" entered the European lexicon as a synonym for "calculation procedure." From his work we also get the word algebra (from al-jabr, "restoration," part of his book's title).

Algorithms existed before Al-Khwarizmi. Euclid, around 300 BCE, described in his Elements the algorithm for finding the greatest common divisor of two numbers—an algorithm that remains the most efficient method known and is still taught in theoretical computer science.

But the formal concept and the name come from Al-Khwarizmi. When someone says "the TikTok algorithm," they're using a word that travels from ninth-century Baghdad.

The 20th century: from calculation to the computer

Ada Lovelace (1815-1852), an English mathematician and daughter of Lord Byron, wrote in 1843 what is considered the first algorithm intended to be executed by a machine: a sequence for computing Bernoulli numbers on Charles Babbage's Analytical Engine (which was never fully built). That note makes her a pioneer of programming, a century before real computers existed.

Alan Turing, in 1936, published "On Computable Numbers, with an Application to the Entscheidungsproblem." The paper formalized the notion of the Turing machine—an abstract model of universal computation—and, with it, what it means for something to be "computable" by an algorithm. The work is one of the founding pillars of computer science.

Donald Knuth, a professor at Stanford, began in 1962 to publish "The Art of Computer Programming"—a monumental project whose first volumes came out in 1968 and that is still being updated. It's the classic reference on structured algorithms.

In the mid-20th century, algorithmic complexity and performance analysis—how long an algorithm takes, how much memory it uses—became a field of their own. The "Big O" notation for describing asymptotic complexity became popular from the 1970s on.

The algorithms that do affect marketing

In 2026, the algorithms that most affect a marketing team aren't the numerical-calculation kind. They're the ranking and recommendation algorithms that decide what content is shown, to whom, and in what order.

Google and search ranking

The seminal algorithm is PageRank, developed by Larry Page and Sergey Brin at Stanford between 1996 and 1998 as the basis for Google. The idea: treat the web as a graph where links are votes, and consider pages with more inbound links more relevant—especially links from pages that are themselves relevant.

PageRank was only the beginning. Google's ranking has gone through decades of evolution:

• Panda (2011): targeted low-quality content and "content farms."
• Penguin (2012): targeted manipulated link spam.
• Hummingbird (2013): reframed search toward semantic understanding of the query.
• RankBrain (2015): the first visible machine-learning component, helping interpret never-before-seen queries.
• BERT (2019): much better natural-language understanding at a contextual level.
• MUM (Multitask Unified Model, 2021): more powerful multimodal models.
• Helpful Content Update (2022): penalizes content written for SEO with no real value.
• AI Overviews (May 2024, expanded in 2025-2026): AI-generated answers above the organic results.

Today "Google's algorithm" isn't an algorithm. It's a system of hundreds of signals and machine-learning models updating continuously.

YouTube and the two decisive metrics

YouTube made the best-known change to its algorithm in 2012, when it shifted from optimizing for clicks to optimizing for watch time. That change reshaped the entire creator economy: short videos and clickbait lost priority; videos that held an audience for minutes gained reach.

Since 2016, YouTube has used deep neural networks for personalization: the system considers not only a user's historical signals but also contextual signals and short- and long-term predictions. The key metric evolved toward valued watch time: watch time weighted by declared satisfaction (surveys, likes, dislikes, "not interested").

Meta (Facebook + Instagram)

EdgeRank, Facebook's public algorithm between 2010 and 2013, used three factors: affinity with the creator, content weight, and time decay. It was explainable. It was replaced by opaque ML systems starting in 2013-2014.

Today Meta combines hundreds of signals per user and per piece of content, with prediction models specific to each content type (text, image, video, Reels). The Reels algorithm, launched in 2020, shares DNA with TikTok: it prioritizes retention and completion rate over affinity with the creator.

TikTok and the For You Page

TikTok has been extraordinarily successful at building an experience where the algorithm decides almost everything. The For You Page (FYP) is the default feed, fed by a recommendation algorithm that personalizes extremely fast—sometimes identifying a user's specific interests in less than 40 minutes of use.

Internal documents leaked to The New York Times in 2021 and additional materials from 2022 described the four core metrics of the TikTok algorithm: likes, comments, playtime, and plays—weighted in a "predicted value" formula for each video. The dominant signal is completion rate (what percentage of the video is watched to the end), followed by rewatch rate (how many times someone watches it in a row).

The consequence for creators: strong hooks, short videos that get watched all the way through, and content that invites rewatching.

LinkedIn

LinkedIn has made several public pivots. In 2018-2020 it prioritized the personal reach of non-commercial accounts. Since roughly 2022, it has tried to balance content from expert creators with corporate content. Its algorithm now prioritizes dwell time (time spent on the post), substantive comments, and relevant second-degree connections.

Paid media algorithms

Covered in detail in another article, but summarized: Google Smart Bidding, Meta Advantage+, Performance Max. Algorithms that optimize bids in milliseconds based on the predicted value of each auction for the advertiser. The human sets the goals; the algorithm decides the mechanics.

Generative AI models

Since late 2022, GPT-3.5/4 (OpenAI), Claude (Anthropic), Gemini (Google), and Llama (Meta) are algorithms in the strict sense—enormously complex, based on the Transformer architecture (Vaswani et al., 2017)—that generate text, images, and code in response to prompts. They've gone from novelty to basic marketing infrastructure in less than three years.

European regulation: the DSA and transparency

The European Union has made its move. The Digital Services Act (DSA)—Regulation (EU) 2022/2065—approved in October 2022 and in force since February 17, 2024 for all platforms, requires large digital platforms (designated "Very Large Online Platforms," or VLOPs, with more than 45 million monthly users in the EU) to provide greater transparency about how their algorithms work.

Some concrete obligations:

• Public documentation of the main parameters of the recommendation algorithm.
• An option for users to choose non-algorithmic feeds (typically chronological).
• Independent annual audits of systemic risk.
• Transparency about advertising and targeting.
• Data accessible to accredited researchers.

The consequence for marketing professionals: for the first time, parts of how the algorithms that affect their campaigns work are officially documented. It's worth reading the Statements of Reasons that TikTok, Meta, Google, and other VLOPs publish periodically; Meta's System Cards; and the Transparency Reports.

Globally, issues like filter bubbles (Eli Pariser, The Filter Bubble, 2011) and addictive design (Tristan Harris, Center for Humane Technology) have put pressure on the industry, though with uneven progress.

How to work with algorithms in marketing

The operational question: what can a team do to "work well" with the algorithms of the platforms it publishes on? Three principles.

Optimize for the algorithm's goal, which aligns with the user's. The algorithms of serious platforms try to maximize user satisfaction (with the difference that they also monetize). So optimizing for real usefulness, genuine retention, completion rate, and substantive comments is aligned with what the algorithm is after. Optimizing for "deception" or vanity metrics rarely holds up.

Understand each platform's dominant metrics. Each channel has a different priority order: TikTok values completion and rewatch; YouTube values valued watch time; Google values helpful content and page experience; LinkedIn values dwell time and substantive comments; Meta varies by content type.

Produce consistently and measure. ML algorithms need data to classify you. An account with sporadic posts doesn't give the system enough signal. A regular cadence—even a modest one—produces better classification than bursts followed by silence.

What does not work in 2026:

• Mass hashtag tricks unrelated to the content.
• Bots and bought engagement: detected with growing reliability and penalized.
• Engagement bait (empty questions to generate comments): algorithms detect and penalize it.
• Reposted content without adaptation: videos with a TikTok watermark uploaded to Reels get less reach.
• Keyword stuffing in SEO: counterproductive since Panda in 2011, and even more so in the BERT/MUM era.

Common mistakes

Treating "the algorithm" as an antagonist. Platforms aren't conspiring against your brand; they're optimizing for users and monetization. Align with that, not against it.

Chasing "hacks" that last weeks. What worked six months ago may not work today. What works consistently is content that's genuinely useful to an identifiable audience.

Confusing reach with impact. A viral post that doesn't convert is vanity. The metric that matters is the business one, not the feed one.

Ignoring the differences between platforms. Replicating the same piece with the same strategy on TikTok, Reels, Shorts, and LinkedIn is waste. Each algorithm prioritizes different signals.

Not measuring. If you publish and don't analyze what works, the learning doesn't accumulate and each piece is a lottery. Basic analysis—retention, CTR, conversion—feeds future decisions.

Assuming that "working with the algorithm" means betraying the brand. A false dichotomy. Brands with a clear voice and positioning do well in modern algorithms when they produce consistently.

Forgetting that algorithms are temporary. Heavily optimizing for pre-Musk Twitter was an investment that evaporated. Optimizing for Vine before its shutdown, the same. Building an owned audience (email, your own site) is a critical complement to renting attention on platforms.

How to fit algorithm knowledge into creative operations

Creative operations are what let a team avoid reacting to every algorithm change by improvising, and instead adjust the system. That means: regular production, templates adapted per channel, metrics that get reviewed, hypotheses that get tested, and knowledge that accumulates.

At Polimake, Studio defines the editorial strategy considering each platform's dynamics; Studio coordinates the calendar and experimentation; Media produces variants adapted to each channel's algorithmic priorities.

This connects with SEO, which is essentially working with Google's algorithm, with bounce rate and other metrics that feed the algorithms, and with the core communication that sustains a coherent voice regardless of channel.

To close

An algorithm is neither magic nor an enemy. It's a procedure—with twelve centuries of history, from Al-Khwarizmi in Baghdad—that modern platforms have scaled into artificial-intelligence systems that decide what gets seen and what doesn't. Working well with them doesn't require tricks; it requires aligning with the algorithm's goal (serving the user well), knowing the dominant metrics per platform, producing consistently, measuring, and building a presence beyond individual platforms.

The practice that ages best: treating algorithms as changing infrastructure you build on—not as stable rules to memorize, nor as hostile systems to hack. The brands that understand this are less volatile when algorithms change, because they don't depend on exploiting specific rules.

Quick references

• Algorithm = a finite sequence of steps to solve a task.
• Etymology: Al-Khwarizmi (Baghdad, 9th century).
• Turing (1936) formalized the mathematical concept.
• PageRank (Page & Brin, 1996-1998) founded modern ranking.
• Google evolution: PageRank → Panda → Penguin → Hummingbird → RankBrain → BERT → MUM → AI Overviews.
• YouTube 2012: pivot to watch time. 2016: deep neural networks.
• TikTok: completion rate and rewatch rate as dominant metrics.
• The EU DSA in force since February 2024: mandatory documentation for VLOPs.
• Don't "trick the algorithm": align with its goal (serving the user).
• Produce consistently: ML needs data to classify you.
• Diversify platforms and build an owned audience (email, your own site).