This case study was done as a part of my class assignment for Introduction of Analytics. It explains how Netflix uses Big Data and why is so successful. Why I chose Netflix Netflix: Stepping into Streaming CLV used in Netflix How Netflix uses Big Data and Analytics Latest Relevant News!! Conclusion

1. Netflix
Analytical CRM Individual Project
Table of contents:
Why I chose Netflix
Netflix: Stepping into Streaming
CLV used in Netflix
How Netflix uses Big Data and Analytics
Latest Relevant News!!
Conclusion
Sources
By
Saket Toshniwal
IÉSEG School of Management

2. Why I chose Netflix
Netflix is an interesting company because it sits in an ever-changing
ecosystem populated by old and new economy players. On one side,
you have movie and TV studios that produce feature-length movies
and serialized TV shows that are, in many ways, identical to the
movies and TV shows that were produced when the medium was
invented. On the other side, you have a rapidly-evolving set of
computer-enabled devices and data transmission systems that allow
consumers to access and stream the studios media content in virtually
any location with a power source and a fast Wifi connection. As a
distributor, Netflix has been forced to evolve with these changes, and
changes in content consumption methods have had a major impact on
the home entertainment ecosystem and the profitability and power of
the players involved.
“There are 33 million different versions of Netflix.”
– Joris Evers, Director of Global Communications
At current count, Netflix has 69.17 million worldwide streaming
customers. Having this large user base allows Netflix to gather a
tremendous amount of data. With this data, Netflix can make better
decisions and ultimately make users happier with their service.
Netflix has Individual Data of each of its customer that enables it to
use the data in the most effective ways. Traditional television networks
don’t have these kinds of privileges in their broadcasting. Ratings are
just approximations, green-lighting a pilot is based on tradition and
intuition. Netflix has the advantage, because being an internet
company allows Netflix to know their customers well, not just have a
“persona” or “idea” of what their average customer is like. Thus, Netflix
is one of the best companies that use customer responsive intimacy
and analytics to leverage itself as a leader in its industry.

4. Netflix: Stepping into Streaming
Beginning in 2007 Netflix began rolling out its content to subscribers
through a video-streaming offering. The innovation was completing
the vision its founder had when creating the Company as illustrated in
his famous quote: “Eventually in the very long term, it's unlikely that
we'll be on plastic media. So, we've always known that, that's why we
named the Company Netflix and not DVDs by Mail." This move to
streaming was well regarded by subscribers, technology enthusiasts,
and Wall Street analysts alike. The rise of Internet video streaming
began.
Netflix launched the Netflix prize, offering $1 million to the group that
could come up with the best algorithm for predicting how its
customers would rate a movie based on their previous ratings.
The winning entry was finally announced in 2009 and although the
algorithms are constantly revised and added to, the principles are still
a key element of the recommendation engine. At first, analysts were
limited by the lack of information they had on their customers – only
four data points (customer ID, movie ID, rating and the date that the
movie was watched) were available for analysis.
As soon as streaming became the primary deliver method, many new
data points on their customers became accessible. Data such as time
of day that movies are watched, time spent selecting movies and how
often playback was stopped (either by the user or due to network
limitations) all became measurable. Effects that this had on viewers’
enjoyment (based on ratings given to movies) could be observed, and
models built to predict the “perfect storm” situation of customers
consistently being served with movies they will enjoy. Happy
customers, after all, are far more likely to continue their subscriptions.
This culture in the organization helped them to identify its customers,
differentiate between them, interact with ‘best-suited’ offers for them
and customize the enterprise behavior to be more customer-centric
approach.

6. Use of CLV at Netflix
Customer Lifetime Value has helped Netflix in multiple ways :
 them what their individual
customer is worth;
 them to estimate the value
of your company’s overall
customer equity;
 enable the company to divide
customers into tangible segments,
separating the most valuable and
committed customers into
different groups and distinguishing
them from the less valuable but
numerous others;
 create opportunities to help marketing managers to refine
marketing practices and ensure that the right approaches are
being made to the right customers;
 them to better predict how certain customers in certain
situations might act going forward; and

retain & develop existing customers, acquire new ones and
reactivate potential sleeping customers.

7. How Netflix uses Big Data and Analytics
Big Data analytics is the fuel that fires the “recommendation engines”
designed to serve this purpose.
1. Predicting viewing habits
Central element to Netflix’s attempt to give us films we will enjoy is
tagging. It pays people to watch movies and then tag them with
elements that the movies contain. It will then suggest you watch other
productions which were tagged similarly to those which you enjoyed.
Netflix has effectively defined nearly 80,000 new “mirogenres” of
movie based on our viewing habits!
Netflix Tracks:
 When you pause, rewind, or fast forward
 What day you watch content (Netflix has found people watch TV
shows during the week and movies during the weekend.)
 The date and time you watch
 Where you watch (zip code)
 What device you use to watch (Do you like to use your tablet for TV
shows and your Roku for movies? Do people access the Just for Kids
feature more on their iPads, etc.?)
 When you pause and leave content (and if you ever come back)
 The ratings given (about 4 million per day)
 Searches (about 3 million per day)
 Browsing and scrolling behavior and a lot more
Netflix uses this data to predict its customer patterns. This data
mining technique helps in cross selling, upselling, responsive
optimization, and a lot in the development phase of aCRM.
2. Finding the next smash-hit series
More recently, Netflix has moved towards positioning itself as a
content creator, not just a distribution method for movie studios and
other networks. Its strategy here has also been firmly driven by its

8. data – which showed that its subscribers had a voracious appetite for
content directed by David Fincher and starring Kevin Spacey. After
outbidding networks including HBO and ABC for the rights to House of
Cards, it was so confident that it fitted its predictive model for the
“perfect TV show” that is bucked convention of producing a pilot, and
immediately commissioned two seasons comprising of 26 episodes.
The ultimate metric which Netflix hopes to improve in the number of
hours that customers spend using its service. This data helps in meta-
tagging to deliver better customer-centric content on Netflix.
3. Quality of experience
To this end, the way that various factors affect the “quality of
experience” is closely monitored and models are built to explore how
this affects user behavior. Improving user experience by reducing lag
when streaming content around the globe, this reduces costs for the
ISPs – saving them from the cost of downloading the data from Netflix
server before passing it on to the viewers at home.
By collecting end-user data on how the physical location of the
content affects the viewer’s experience, calculations about the
placement of data can be made to ensure an optimal service to as
many homes as possible. Data points such a delays due to buffering
(rebuffer rate) and bitrate (which affects the picture quality – if you’re
watching a film on Netflix that suddenly seems to switch from razor-

9. sharp HD to a blurry mess, you’ve experienced a bitrate drop) are
collected to inform this analysis.
Netflix has used Big Data and analytics to position itself as the clear
leader of the pack. It has done this by taking on other distribution and
production networks at their own game, and trumping them through
innovative and constantly evolving use of data. Many managerial
questions can be asked and answered with the use of descriptive,
predictive, and prescriptive analysis.
4. Defining future plan of action
Netflix collects customer insights from customers to improve its
operational, analytical, and strategical CRM policies. Creative
techniques are used with Analytical CRM to improve business
performance.
Netflix uses optimum marketing campaigns that impacts the individual
customers the most. For example, it identifies which customers spend
more time on television, ipads, mobile, desktop and other digital
devices. This identification is done by the number of hours spend
streaming through Netflix on different devices by each individual
customers. Thereafter, it sends marketing campaigns to their
customers that impact them the most with highest ROI for Netflix.
Netflix has an 80 percent success rate (at the very minimum) with
original programming, compared to the 30 to 40 percent success rate
for networks. These shows have primarily been picked by running data
mining and other algorithms against the vast user behavior data
available to determine the size of the possible audience and thereby
the likelihood of success.

10. Latest Relevant News
This news is just released today. It shows how Netflix is using data
from human behaviors to give a better customer experience.
Netflix socks to pause show if user dozes-off
News Released on : 10:53 pm on 17 Dec 2015,Thursday
“Netflix has developed a new censor-fitted pair of socks which will
pause the running show on Netflix if the user falls asleep, resulting in
no leg movement. The socks would be fitted with an LED indicator too
which will let the user know, in cases of false positive, that the current
show will be paused.”

11. Conclusion
Now you see how Netflix makes informed decisions based on data.
Clearly, data cannot make every decision; there are some situations
where intuition has to take over. For instance, data could not predict
that a show like Breaking Bad would be a success. The creator was a
former writer on The X-Files, and dramas are 50/50. In these cases,
decisions are heavily based on the people and team behind the idea of
the show. Whether Netflix can make a successful show like this (one
with little to no data) is yet to be seen.
What analytics and data can do is give you insight so you can run a
better business and offer a superior product. People with data have an
advantage over those who run on intuition or “what feels right.”
Do you have data to help you make decisions? If not, Netflix provides a
good case for why you should do so. Netflix Rocks! Analytics Rocks
Harder!!

12. Sources
https://blog.kissmetrics.com/how-netflix-uses-analytics/
https://getpocket.com/a/read/902639174
https://pr.netflix.com/WebClient/loginPageSalesNetWorksAction.do?c
ontentGroupId=10477
Thank You for you patient reading.
Saket Toshniwal
IÉSEG School of Management
MSc Digital Marketing and Customer Relationship Management 2015-
16
Date : 17th Dec 2015