What if you work with services?

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You'll be able to guide your customer service team by predicting demand and churn to know which parts of the service aren't delivering value, and measure your CAC much more accurately with predictive insights into your customers' satisfaction and success.

A significant example of a company that makes good use of the Data Driven culture is Amazon. Jeff Bezos' company uses it to:

1) Offering users a meaningful experience through recommendation algorithms, which are built based on behavioral analysis of customers, ensuring that products that best fit their profiles are offered. This tactic, together with the one-click purchase button (called the million-dollar button), made it more difficult to leave the page without buying anything.

2) By analyzing the data from your logistics chain, Amazon built a patent on a predictive model that can anticipate the merchandise needs of its warehouses, in this way it is prepared to always serve its customers as quickly as possible, reducing the company's logistics costs.

3) The company also manages to be much more competitive in relation to the lower price of its products. By analyzing its vast amount of information, it is possible to create an optimization of the company's offers, understanding when and why there is less competition in its market.

Netflix and its strange things
Stranger Things
Source: www.b9.com.br

Surely you must know what the Stranger Things series is , one of the series from the giant Netflix.

Maybe what you don't know is that this series was written entirely based on data , in the end, in case you have seen any movie from the 90s like, The Thing, ET the Extraterrestrial and Alien, you will notice the various references used in the narrative of Stranger Things.

Clearly Netflix is ​​not just a movie company, but a big data company.

After all, everything from scripts, characters, trailers and even every image that appears on your screen is based on data analysis and recommendation algorithms.

Some people, like me, believe that Netflix's series and movie projects are written by a data analysis and machine learning algorithm that writes a script with everything that a certain audience wants to see, making it possible for directors to be much more creative, through a good update of Big Data.

According to what I learned in the presentation by Michelle Ufford, Engineering Manager at Netflix, as of October 2016 the company had 86.7 million members, supporting over 1000 types of input devices (smartphones, tablets) and over 125 million hours watched per day.

It currently has more than 125 million users.

Today that number is probably much higher, and you can clearly notice argentina phone data that content is becoming more segmented, bad movie recommendations are becoming fewer and fewer, and the user experience and usability is improving every day.

Netflix co-founder Reed Hasting likes to say that when you're coaching someone on data, you need to use analytics, leading to focus, analysis, and money.

By studying the use of predictive analytics in the company we can conclude some of its applications. Therefore, its data sources consist of:

Your viewing experience: You probably rewind to a scene, fast-forward through an episode to see the ending, or pause the series at a certain point;
The exact moment you see content: Knowing the exact date and time your customers use your services allows companies to learn much more about their users;
The device you use Netflix on is also very important in understanding user habits;
Yes, the company also uses machine learning and data analytics applications for its UX and UI. Knowing the browsing behavior of users along with the launch brings many insights;
Perhaps one of the most important variables on Netflix is ​​the rating users give to movies. The company at one point discovered that two buttons, one for like and one for dislike, provide much better information than the 1 to 5 rating.