Why Personalization Became the New Normal — And What’s Next with AI

Where It All Started — The Early Days of Personalization

The late ‘90s marked the start of something big. Amazon pioneered large-scale recommendation systems, driven by a simple but powerful idea — use what people like to suggest what they might love next. Their iconic “Customers who bought this also bought…” feature wasn’t just smart; it was a game-changer. By 2006, 35% of Amazon’s sales came from these recommendations alone. For the first time, users felt like a platform knew what they wanted — or at least could guess.

Back then, personalization was mostly rule-based — if X, then Y. Limited data, limited processing power. But it worked because it introduced a powerful idea: people like feeling understood.

The Rise of Data and Recommendation Engines

As the 2000s rolled in, platforms got better at collecting data. Netflix became a household name not just for its content, but for its collaborative filtering algorithm, Cinematch — learning from what you watched to suggest what you might like next.

In 2006, they launched the Netflix Prize — offering $1 million to anyone who could improve Cinematch by 10%. It wasn’t just a stunt; it sparked a global leap in machine learning research. And Netflix proved just how valuable getting personalization right could be.

Personalization Gets Smarter — and Spreads Everywhere

By the mid-2000s, personalization was moving beyond products to content, search, and even social connections.

Google’s personalized search for logged-in users rolled out in 2005, expanding further by 2007. By personalizing search results, it made every query feel a little more like it "just knew" what you meant. YouTube’s early recommendation system evolved rapidly, shifting from simple views and likes to deeply analyzing watch time and engagement — driving over 60% of video clicks by 2012.

Social platforms caught on fast. Facebook’s News Feed, launched in 2006, transformed how we consume content — bringing the most relevant updates directly to users. Engagement skyrocketed. Pinterest, meanwhile, pioneered visual discovery, using image recognition to recommend pins based on what users were drawn to — not just what they searched for.

The Boom: Music, Social, and the Rise of Taste Profiles

From 2011 onwards, personalization wasn’t just a feature — it was the product.

In 2015, Spotify changed music discovery forever with Discover Weekly — a personalized playlist built just for you, refreshed every Monday. Behind the scenes? A proprietary algorithm analyzing billions of playlists to map cultural music trends and your unique taste profile.

Instagram shifted from a simple feed to a fully algorithm-driven experience around 2016, surfacing posts, stories, and even “close friends” based on how you engage.

LinkedIn brought the same logic to professional connections around 2011–2012 — turning “People You May Know” into a powerful networking tool that boosted connection rates.

Personalization Today: AI Takes the Lead

The last few years have been a turning point. Personalization, once rule-based and reactive, is now driven by LLM and predictive systems — real-time and incredibly nuanced.

TikTok’s algorithm, launched around 2018, set a new bar. It could understand your preferences within the first hour of interaction — something that took other platforms weeks. It wasn’t just good — it was addictive, forcing the whole industry to rethink how fast and how well AI could learn user behavior.

Other platforms followed suit. Snap’s Dynamic Stories in 2021 combined breaking news with real-time personalization. Anthropic’s Claude system (2022) personalized not just content, but the way AI interacted — adapting tone, style, and depth to each user. And OpenAI’s GPT-4o (2024) started to test persistent memory, allowing AI to learn and adapt over time — turning every interaction into a smarter, more tailored experience.

What Does This Mean for iGaming?

The evolution of personalization offers a clear takeaway for iGaming industry: user’s expectations have changed. Today’s people are used to apps and platforms that just get them. Having grown up in a digital world, they expect recommendations to be relevant, bonuses to match their behavior, and experiences that feel designed for them — not just the crowd.

The good news? AI has unlocked new ways to deliver that — in real time, at scale, without guesswork. Platforms that embrace it don’t just boost engagement and retention — they build loyalty. And that’s what keeps players coming back.

What started as a clever e-commerce trick is now a standard across industries. From Amazon’s product suggestions to TikTok’s For You page, personalization has redefined how we engage with digital experiences.

For iGaming, it’s no longer a question of if personalization matters — it’s about how fast platforms can put it to work. Because players know what personalized feels like — and they’ll stick with platforms that deliver it.