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

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.

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.

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.

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

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-

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.

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.”

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!!

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