From Spotify knowing your mood, to Lazada (a popular shopping app in Southeast Asia) quietly tipping off Facebook about what you almost bought — here’s how your favorite apps use your data to figure you out.
Wait… Are My Apps Psychic?
Ever feel like your apps know a little too much about you?
Netflix recommends a sad movie right after your breakup.
Spotify plays the exact song you didn’t know you needed.
You browse a pair of sneakers on Lazada… and now Facebook won’t stop showing them to you.
Creepy? Kinda.
Magical? Also yes.
But here’s the truth: it’s not magic — it’s data.
These platforms are built on tracking, learning, and predicting your behavior. Let’s break down how they do it — no tech degree required.
Step 1: You Leave Digital Footprints Everywhere
Every time you:
- Rewatch a scene on Netflix
- Add a gadget to your Lazada cart
- Pause a song on Spotify
- Scroll Instagram ads
- Watch 12 TikToks in a row at 1AM
You’re creating data points — little digital breadcrumbs that say something about you. Where does that data go? Into cloud storage — think of it like a massive online vault where platforms collect and store all those clicks, scrolls, views, likes, and purchases.
Quick Side Note: What’s Lazada?
If you’re outside Southeast Asia, Lazada is essentially the Amazon of the Philippines, Malaysia, Indonesia, Thailand, and more. It’s a go-to online shopping platform for everything from gadgets to skincare, and yes — it knows a lot about what you browse, click, and leave in your cart.
Step 2: The Data Gets Cleaned and Organized
Raw data is messy. So behind the scenes, data engineers run a process called ETL:
- Extract the data from apps and trackers
- Transform it — clean it up, fix errors, remove duplicates
- Load it into structured databases so it’s ready for analysis
Think of it like washing and prepping vegetables before cooking.
No one wants raw carrots from the ground. You need clean, organized ingredients first.
Step 3: The Algorithm Learns Who You Are
Once the data is clean, smart systems (called algorithms) start connecting the dots. Platforms use machine learning to recognize patterns in what you watch, play, or buy — then predict what you’ll want next.
For example:
- Netflix sees that you watched 3 rom-coms and a stand-up special last Friday. It suggests something with both: a funny love story.
- Spotify notices you play mellow acoustic songs every Monday morning. It builds you a playlist to match.
- Lazada tracks that you view kitchen appliances around payday — and shows you a flash sale at just the right time.
These predictions aren’t guesses — they’re based on behavior, timing, and people who act like you.
Step 4: When Lazada Snitches to Facebook
Now let’s talk about the real stalker energy: seeing a Lazada product follow you to Facebook or Instagram.That happens because of something called the Meta Pixel.
What’s a Meta Pixel?
A Meta Pixel is a tiny, invisible piece of code that websites (like Lazada) install to track what you do on their platform.
It can see if you:
- Viewed a product
- Added something to your cart
- Scrolled through a promo
- Abandoned checkout
That data is sent to Meta (Facebook and Instagram’s parent company). Meta then matches it with your Facebook or IG profile — and boom: you start seeing ads for the exact product you almost bought. It’s called retargeting, and it’s very effective.
Lazada: “Marvin checked out sneakers but didn’t buy.”
Facebook: “Say less.”
What Each Platform Knows and How They Use It?
Netflix
- Tracks watch history, time of day, pause/rewind/skip behavior
- Adjusts thumbnails per user based on what you’re likely to click
- Builds recommendations using both content similarity and other users’ behavior
Spotify
- Analyzes the tempo, mood, and energy of songs
- Tracks when and how often you listen
- Curates mood-based playlists like Discover Weekly and “On Repeat”
Lazada
- Monitors searches, cart activity, page views, and wishlists
- Sends behavioral data to ad networks (like Meta)
- Personalizes homepage deals and timed promotions
Facebook (Meta)
- Uses data from FB, IG, and partner apps
- Tracks your activity across the web (thanks to the Pixel)
- Optimizes ad delivery based on your behavior and profile
Should You Be Concerned?
A little. But also… maybe not? You’re benefiting from these insights:
- Better suggestions
- Less scrolling
- Custom deals
- Relevant playlists
- Spot-on ads (sometimes too spot-on)
But you’re also giving up some privacy in exchange for convenience.
Good news: you can manage your ad preferences on Meta, or opt out of tracking on many platforms. But even if you do… the algorithms still learn from general patterns and anonymous data.
Final Thoughts: Creepy, Useful, or Both?
These apps don’t need to “read your mind.” They don’t have to. You told them everything — through your clicks, skips, scrolls, and playlists.
What feels like magic is really:
- Smart data collection
- Clean pipelines
- Machine learning
- Personalization engines
- And a bit of behavioral psychology
So next time Netflix recommends the perfect sad movie, Spotify builds a cry-in-the-car playlist, or Lazada gets Facebook to remind you about those shoes…
Just whisper: “I see you, Pixel. I see you.”
About the Author
Marvin Baesa is a Business Intelligence Analyst and Data Expert solutions based in the Philippines. He works closely with the U.S. and Australian based companies across a wide range of industries—including e-commerce, legal services, marketing agencies, real estate, manufacturing, and logistics—helping teams transform raw data into actionable insights.
With over 5 years of experience in data analytics, reporting automation, dashboard development, data engineering, and process automations and optimization, Marvin is passionate about making data make sense and accesible. Whether it’s uncovering hidden trends in sales, cleaning messy CRM exports,automating manual reports and processes or building dynamic dashboards, his goal is always the same: to turn data into decisions.
He is the founder of DataWithMarvin.com, a platform where he shares no-fluff insights on analytics, data science, reporting best practices, and real-world business applications—with the occasional touch of humor and real talk from the trenches.
When he’s not elbow-deep in SQL queries or series of questions that makes his life exciting, Marvin is mentoring new analysts, brainstorming witty t-shirt ideas for his apparel brand, or helping companies scale through smart outsourcing via Subconify Solutions.
📫 Connect with Marvin: engr.marvinbaesa01@gmail.com
Bravo! Such an insightful article. Thanks for acquainting me with that Pixel thing. 🤘😎