“Analytics is all around us!” – “We benefit from analytics every day!” – “Data is a powerful thing!”
You have probably heard many similar statements. But you do not know where analytics is used in real life? The answer is very simple:
- Everywhere!
Since?
- 19th century (believe it or not!)
For any company or organization, data collection is essential! Sales, finance, marketing, fulfilment…… everyone needs analytics today.
I read somewhere that oil is no longer the world’s most valuable resource but data.
This blog is not about how to create analytics competency in your organization (I will write this post in the future). It’s not about what metrics to track (you can find plenty of good posts about that).
Through this article, you will see how data analytics (predictive analytics also, as a subset of data analytics) helped the world’s largest companies, which achieved millions of revenues, to become what they are today.
Real-life situation:
Netflix
Everyone heard of American provider Netflix. Once you log in and pay your monthly fee, moves you can select can be watched instantly.
By the end of 2020, Netflix had 195 million people subscribed to its streaming services, and 73 million subscribers were from the US.
Netflix revolutionized the way television content is created for audiences, and the did that using Analytics (believe it or not ?)
They first started by collecting data. Lots, lots, loooooots of data!
They had access to the view in preferences of all their members. Netflix made great use of it. By analyzing data, they got a chance to determine what kind of people want to watch what genre.
How did they do that?
Netflix’s extensive analysis led them to conclude that David Fincher, the director of “The Social Network”, was very popular.
On the other side, the British version of “House of Cards” (political tv drama) from the ‘90s had been well rated by existing viewers.
And then, what about that?
Then they noticed that many viewers of both David Fincher and the British “House of Cards” also watched films starring Kevin Spacey. So no matter that they are very individual, Netflix had a lot of users in each category.
These conclusions led Netflix to make the decision to buy the rights to the show to make an American version of “House of cards.”
That’s not where analytics is completed.
Netflix further customized the experience by having different ads and promotions for the series tailored to the different types of viewers. Spacey fans started to see a lot of Kevin Spacey in the advertisements. Fans of the director were reminded of his other films.
It bought 2M new US subscribers in the 1st quarter of 2013. 7% increase over the previous quarter. It also accepted 1M new subscribers from all over the world.
Netflix is constantly collecting data. Netflix uses AI-powered algorithms to make predictions based on the user’s watch history, search history, demographics, ratings, and preferences. These predictions show with 80% accuracy what the user might be interested in seeing next.
Tesla
Tesla is an electric vehicle company that’s transforming the automobile market.
Tesla’s onboard computer runs a neural net for vision, sonar and radar processing systems. Tesla has also adopted fleet learning to improve the analytics process.
In addition, Tesla uses deep neural network algorithms to train its autopilot with collected real-world data.
Nintendo
For hardcore gamers, analytics is also used for games. Ooo Yes! You heard right. Japanese company Nintendo uses nonstandard chip DLSS (deep learning super sampling). The chip analyses data and dynamically enhances graphics and renderings so users can experience high-quality visuals with minimal lag.
Spotify
When you sign up for a Spotify account, your first instinct is to search for your favourite song and listen to it, right? Well, Spotify uses this information to make recommendations to you every week. For example, their Discover Weekly playlist allows users to listen to songs of a similar genre based on what the user listens to daily, what they’re “heart-ing” on the app, and what other listeners of the same genre hear. The more users use Spotify, the more personalized their playlists get. It’s an excellent example of the predictive analytics segmentation model.
In the last couple of years, Spotify has launched the Spotify Wrapped campaign. That gives users a summary of their past year’s listening habits and the possibility to instant share on social media. Perfect marketing but also an excellent way to place data in the spotlight. Using data analytics, the company can tell users if they’re one of a band’s most loyal followers or if they discovered popular music ‘before it was awesome’, et Cetera.
Sephora
Sephora is probably more interesting for the ladies.
Sephora, a French multinational retailer of personal care and beauty products, understands how overwhelming finding new makeup and beauty products can be, especially for a beginner.
So, it uses a combination of predictive analytics tools and techniques to guide its customers through its catalogue. Based on interests, purchases, preferences, and Color-ID and Skin-ID technologies.
Sephora can create personalized profiles for each customer and curate an almost accurate “Recommended for you” page like “new for you”, “similar products,” or “you may also like” suggestions, and “recently viewed” feed of recommendations.
Amazon
Well-known e-commerce platform. Amazon stores every single piece of information related to the customer. They use big data and analytics to handle product prices to attract more customers and increase the net profit. You must have noticed that when you want to put a product on the list or add a product to the basket, you automatically get a recommendation of an item related to your product or suggest which product would be good to buy. These possibilities are available today in other large businesses such as Ikea.
Apple
For Apple, we could say it is an expert in using advanced technologies. By collecting data, Apple can see how people use their application in real life so they can change future designs to fit with customer preferences.
Two critical areas of using big data and analytics by Apple are:
- App design and tracking people’s health so they improve their lifestyle.
Apple Watch is now one of the main things for collecting data daily. They can track what users do during the day.
As you notice, big data is being collected to analyse and use in social media advertising algorithms that can be further used to expand customer relations, recommend products, improve customer experience and services, etc.
