Introduction

The holidays are here, and normally this is the season of rushing, guessing, and hoping you picked the right gifts. But with AI in the mix, the whole experience is changing. AI now plays a major role in how people discover gifts, entertainment, and deals during the holidays.

This marked shift is quite pronounced, with 75% of shoppers predicted to use AI to find deals this holiday season, up from 66% in 2024. If this reveals anything, it is that people are now comfortable with machine-guided decisions. Cos why not? It makes perfect sense that AI handles all the heavy lifting by filtering through an avalanche of options, comparing prices, studying behaviours and patterns, and bringing forward whatever aligns with a person’s taste or interests.

That is why personalization often feels like magic. I like to see it as mind reading. The suggestions you get often match your mood, your habits, and even the things you forgot you liked. This “mind reading” comes from data and pattern-recognition working at scale. And the more you interact with these systems, the better they get at understanding what you’ll click, watch, or buy next.

Why Netflix, Amazon, and Spotify?

If you want to understand how AI seems to always read your thoughts, look no further than Netflix, Amazon, and Spotify. These three industry powerhouses built their entire experience around it. Because we use them every day, they’ve rightly become the yardstick for personalization across various industries.

Netflix controls how we binge, Amazon shapes how we buy, and Spotify influences what we listen to. They’ve each perfected the art of collecting signals from millions of users and turning those signals into predictions that are almost always accurate.

Together, they’ve set the global standard for recommendation technology. Their systems are so advanced that other industries now reference them as the benchmark for smart experiences. So when you’re holiday shopping and get magical accurate suggestions, remember that the algorithm is aided by data from learned patterns from previous purchases.

Netflix Case study

Netflix’s biggest strength comes from its recommendation system. About 75% of everything people watch comes from what the platform suggests, not what users manually search for. That alone shows how accurate their system has become. Netflix runs on three major machine learning techniques that work together to create a personalized viewing experience:

Collaborative filtering analyzes viewers who watch similar things as you. If viewers with your exact taste binge a show you haven’t watched yet, Netflix pushes it your way.

Content-based filtering studies the characteristics of each title. Every title on Netflix is broken down into tiny attributes: genre, cast, mood, themes, language, pacing, and more. The system examines the specific characteristics you consistently engage with, then pushes more content with similar attributes as the one you watch.

Reinforcement learning acts as a fine-tuner. Netflix watches how you interact with content in real time. Every play, skip, rewind, becomes feedback. The system learns from your behaviour and adjusts what it recommends next.

These three techniques are what makes you feel like Netflix is one step ahead of you. However, the personalization doesn’t stop at the titles. Netflix also uses personalized thumbnails, displaying different artwork to different users. The system tests multiple versions and picks whichever image you’re most likely to click based on your past behaviour.

Amazon Case study

Once you land on Amazon’s site to shop, you immediately notice the shift in your entire shopping experience compared to other e-commerce stores. This is ultimately down to Amazon Personalize, a system built to deliver real-time predictions based on your browsing, purchases, and patterns. It learns what you buy, how you search, and even how long you hover on an item.

In 2024, Amazon took it up a notch with generative AI. Instead of generic “You might like this” suggestions, the site now creates shopping ideas tailored to your daily life. You get things like “Gift ideas for your travel goals” or “Deals to upgrade your workspace,” influenced directly from your activity.

Even product descriptions are smarter now. Amazon’s large language models rewrite titles and descriptions to highlight the features you personally care about. Instead of just seeing a product, you are seeing the version of that product that makes sense for you. The strategy clearly pays off. 91% of customers prefer personalized shopping, and Amazon has saved over $1 billion through personalization because it boosts retention.

Spotify Case study

Spotify recommends songs in a way that builds a listening experience that feels personal. Sometimes it feels as though it reads your mood and recommends songs that blend with it. “Discover Weekly”, a playlist curated on Mondays, alone reaches over 100 million people, and for many listeners, the playlist is always on point. The engine behind Spotify's recommendation system runs on three powerful layers:

Through Collaborative filtering, Spotify looks at what millions of users with similar music habits are listening to. If songs you love consistently appear next to other tracks on people’s playlists, those tracks become strong candidates for your recommendations. That is how new artists pop up in your playlist out of nowhere.

Spotify scans articles, blogs, social conversations, and metadata to understand how people describe music. It uses Natural Language Processing to analyze the emotions, themes, and cultural context around songs and artists. For example, when critics call a song “perfect for late-night drives,” Spotify uses that info to match it to listeners who crave that exact vibe.

With audio analysis, Spotify breaks down the actual sound. The system studies energy, tempo, mood, loudness, key, danceability and more. This lets the system match songs that sound similar, not just songs people label as similar.

These three key engines have made Spotify one of the biggest systems for artist discovery worldwide. Discover Weekly accounts for 20% of Spotify’s entire streaming volume, generating over 5 billion annual streams. And the truth is that users who engage with these personalized playlists are likely to stick around long-term.

AI and Holiday Shopping Behavior

It is safe to assume that holiday shopping is in its AI era. As established in the introductory part of this blog, this season, 75% of consumers will use AI to find deals, showing how quickly people are shifting toward automated decision-making.

It’s not just the convenience though. Shoppers say AI actually affects how they feel while shopping. 69% feel happier using AI tools and 70% feel less anxious, which is quite interesting but makes sense. When you have your choices curated for you, the stress drops.

On the side of the retailers, they see AI as a tool to win the loyalty of customers in a competitive market. Personalization is now a core strategy, with 19% of all holiday orders influenced directly by AI-driven recommendations. Holiday 2024 sales hit $1.2 trillion globally and $282 billion in the US, a clear indication that AI-powered discovery played a role in pushing those numbers up. Almost everything in the market today is algorithm-driven.

Conclusion

Holiday shopping has become a lot more seamless. With AI, the whole experience now feels somewhat like a guided tour. Industry giants like Netflix, Amazon, and Spotify have shown the world what effective personalization feels like, and now that same intelligence is transforming how people find gifts, deals, and entertainment.

Shoppers are leaning into it because the experience is simply better. Less stress, fewer bad decisions, and more spot-on recommendations that actually match who they are. Retailers are leaning into it because personalization drives customer retention and sales. And as the numbers show, AI is already influencing a huge share of holiday spending.

The season is still all about joy, connection, and thoughtful giving, and with AI now making the process smoother, it just got better from here.