Hello Hello Hello,
I have a little secret to share: Yesterday, during the longest day of the year, I may have dedicated the entire day to indulging in Netflix. (Promise not to tell anyone? 🤫)
But here's the million-dollar question: How on earth does Netflix manage to have such a grip on us, making it practically impossible to resist that tempting "Next Episode" button?😳
It’s a no-brainer that it all is the artwork of big data & machine learning.
Think about it: From personalized recommendations based on your viewing history to a seamless user interface - Netflix has mastered the art of keeping us glued to our screens.
And in today's edition, we'll explore how Netflix is using AI & ML to fuel our addiction and what retailers can learn from it:
So, grab your popcorn, and let’s start the show🍿:
📽️ User-based Personalization: Now Streaming - The Netflix Way
Netflix has set the bar high when it comes to personalization and grabbing our attention. It’s one of the standing pillars of the brand.
From customizing the home screen for each & every of its 692.10 million viewers globally to providing a tailored experience, the platform has made each member have a different view of the content that adapts to their interests.
Not only that but even the Netflix search capacities, the messages we receive, everything is centered around each user.
And ... this is all done using two elements: “Data” & “AI Algorithm”
Let’s discover how:
🍿 4 Lessons to Learn from Netflix Personalization:
Let's explore 4 ways how Netflix is winning the personalization game –
- 🔢 Where Data Know You Better Than Your F.R.I.E.N.D.S. Do
Everything Netflix does is driven by data and powered by smart AI algorithms. The more you watch, the more data they fetch, the more personalization they do.
Netflix gathers all types of including basic demographic, time & type of device, information including how long viewers scroll the main page, etc. to generate comprehensive viewer profiles to understand them better & thus provide a hyper-customized experience using AI algorithms;
Making it easier and faster for Netflix users to find what they want & stick to the platform repeating the data collection cycle.
- 💻 Strong Recommendations Take Center Stage (Left, Actually)
Netflix uses the viewer’s ranking, search history, and rating data as well as time, date, and the kind of device a user uses to make a tabular Recommender System:
- The strongest recommendations are shown on the left within each row
- The strongest recommendations are shown on top across each row
Each row highlights a particular theme as is generated based on the user’s past activity & single algorithm. The advantage of using the rows are simple: users get more coherency, and the brand gets better interest/disinterest feedback from user scrolling
- 📺 SRK vs Alia: Netflix's AI Picks Your Poster
Netflix used image recognition that stores, analyzes, and personalizes still images and images within videos that users prefer, watch, and rewatch.
This means Netflix's advanced machine learning technology can identify and categorize objects in scenes, moods, and genres that users are often drawn to & then uses this information to create personalized artwork & marketing specifically tailored to viewers' preferences.
For eg: Someone who is a romance buff might want to watch SRK's title in “Dear Zindagi” more than that of Alia Bhatt. Netflix does exactly that based on the user’s behavior.
- 📹 Netflix’s Game of Right Content at the Right Place
Content is King, and has the power to make or break the overall user experience. Netflix understands this very well. It uses content to map the success or failure of its recommendations based on how users are liking or disliking them.
For instance, if a user shows a strong interest in watching horror movies, Netflix will suggest similar movies to keep the user engaged on its platform.
What we can understand from all of these is Netflix has mastered the customer experience & besides watching movies – there’s still a lot to learn from it!✋🏽
📚Story of the Week: 5 things you are doing wrong with Customer Segmentation
In today's crowded market, businesses often get caught up in the rush to reach everyone quickly, which can make them lose sight of their ultimate goals and desired outcomes.
However, implementing effective customer segmentation can be a real game-changer. But if the results aren’t anticipated, chances are you might be making some common mistakes.
Check out our latest blog that uncovers 5 common mistakes brands make when segmenting their customers. Read: << 5 things you are doing wrong with Customer Segmentation >>
That’s all for today.
And oh! Please do let me know which shows you are currently watching. I am desperately in need of some good recommendations🥲
Until next time.